U.S. patent application number 16/695595 was filed with the patent office on 2020-03-26 for fall protection equipment event generation and monitoring.
The applicant listed for this patent is 3M INNOVATIVE PROPERTIES COMPANY. Invention is credited to Nathan J. Anderson, Matthew J. Blackford, Jia Hu, Ronald D. Jesme, Keith G. Mattson.
Application Number | 20200096952 16/695595 |
Document ID | / |
Family ID | 60162324 |
Filed Date | 2020-03-26 |
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United States Patent
Application |
20200096952 |
Kind Code |
A1 |
Hu; Jia ; et al. |
March 26, 2020 |
FALL PROTECTION EQUIPMENT EVENT GENERATION AND MONITORING
Abstract
In some examples, a system includes a self-retracting lifeline
(SRL) comprising one or more electronic sensors, the one or more
electronic sensors configured to generate data that is indicative
of an operation of the SRL; and at least one computing device
comprising one or more computer processors and a memory comprising
instructions that when executed by the one or more computer
processors cause the one or more computer processors to: receive
the data that is indicative of the operation of the SRL; apply the
data to a safety model that predicts a likelihood of an occurrence
of a safety event associated with the SRL; and perform one or more
operations based at least in part on the likelihood of the
occurrence of the safety event.
Inventors: |
Hu; Jia; (Mounds View,
MN) ; Blackford; Matthew J.; (Hastings, MN) ;
Mattson; Keith G.; (Woodbury, MN) ; Jesme; Ronald
D.; (Plymouth, MN) ; Anderson; Nathan J.;
(Woodbury, MN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
3M INNOVATIVE PROPERTIES COMPANY |
St. Paul |
MN |
US |
|
|
Family ID: |
60162324 |
Appl. No.: |
16/695595 |
Filed: |
November 26, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15782645 |
Oct 12, 2017 |
10496045 |
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16695595 |
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62408442 |
Oct 14, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A62B 35/0043 20130101;
G05B 9/02 20130101; G05B 6/02 20130101; A62B 35/0093 20130101; G05B
17/02 20130101 |
International
Class: |
G05B 9/02 20060101
G05B009/02; G05B 17/02 20060101 G05B017/02; G05B 6/02 20060101
G05B006/02; A62B 35/00 20060101 A62B035/00 |
Claims
1. (canceled)
2. A system comprising: a self-retracting lifeline (SRL)
comprising: a first connector configured to be coupled to an
anchor; a second connector configured to be coupled to a user of
the lifeline; one or more electronic sensors, the one or more
electronic sensors configured to generate usage data that is
indicative of an operation of the SRL; a first communication
component; and a data hub comprising: a second communication
component; one or more computer processors; and a memory comprising
instructions that when executed by the one or more computer
processors cause the one or more computer processors to: receive,
from the first communication and by the second communication
component, usage data for the SRL; apply the usage data to a safety
model stored in the memory, the usage data characterizing a force
applied to the lifeline of the SRL based on activity of a user of
the SRL; determine, based on identification of anomalous behavior
of the user of the SRL relative to known safe behavior
characterized by the safety model, an occurrence of a safety event
associated with the SRL based on application of the usage data to
the safety model, wherein the anomalous behavior of the user is
characterized by the force applied to the lifeline of the SRL over
a time period exceeding a force associated with safe activity of
the SRL over the time period; and perform at least one operation
based at least in part on determining the occurrence of the safety
event associated with the SRL.
3. The system of claim 2, wherein the safety model is constructed
from historical data of known safety events from a plurality of
SRLs having similar characteristics to the SRL.
4. The computing device of claim 2, wherein the memory comprises
instructions that when executed by the one or more computer
processors cause the one or more computer processors to update the
safety model based on the usage data from at least one of the SRL
or one or more devices other than the SRL.
5. The computing device of claim 2, wherein the memory comprises
instructions that when executed by the one or more computer
processors cause the one or more computer processors to select the
safety model based on at least one of a configuration of the SRL,
the user of the SRL, an environment in which the SRL is used, or
one or more other devices that are in use with the SRL.
6. The computing device of claim 2, wherein to apply the usage data
to the safety model the memory comprises instructions that when
executed by the one or more computer processors cause the one or
more computer processors to apply the usage data to the safety
model that is constructed from training data of known safety events
associated with a plurality of SRLs.
7. The computing device of claim 2, wherein to perform the at least
one operation the memory comprises instructions that when executed
by the one or more computer processors cause the one or more
computer processors to generate at least one alert for output.
8. The computing device of claim 2, wherein to perform the at least
one operation the memory comprises instructions that when executed
by the one or more computer processors cause the one or more
computer processors to send a message to a remote computing
device.
9. A self-retracting lifeline (SRL) comprising: a first connector
configured to be coupled to an anchor; a second connector
configured to be coupled to a user of the lifeline; one or more
electronic sensors, the one or more electronic sensors configured
to generate usage data that is indicative of an operation of the
SRL; one or more computer processors; and a memory comprising
instructions that when executed by the one or more computer
processors cause the one or more computer processors to: receive,
from the one or more electronic sensors, usage data for the SRL;
apply the usage data to a safety model stored in the memory, the
usage data characterizing a force applied to the lifeline of the
SRL based on activity of a user of the SRL; determine, based on
identification of anomalous behavior of the user of the SRL
relative to known safe behavior characterized by the safety model,
an occurrence of a safety event associated with the SRL based on
application of the usage data to the safety model, wherein the
anomalous behavior of the user is characterized by the force
applied to the lifeline of the SRL over a time period exceeding a
force associated with safe activity of the SRL over the time
period; and perform at least one operation based at least in part
on determining the occurrence of the safety event associated with
the SRL.
10. The SRL of claim 9, wherein the safety model is constructed
from historical data of known safety events from a plurality of
SRLs having similar characteristics to the SRL.
11. The SRL of claim 9, wherein the memory comprises instructions
that when executed by the one or more computer processors cause the
one or more computer processors to update the safety model based on
the usage data from at least one of the SRL or one or more devices
other than the SRL.
12. The SRL of claim 9, wherein the memory comprises instructions
that when executed by the one or more computer processors cause the
one or more computer processors to select the safety model based on
at least one of a configuration of the SRL, the user of the SRL, an
environment in which the SRL is used, or one or more other devices
that are in use with the SRL.
13. The SRL of claim 9, wherein to apply the usage data to the
safety model the memory comprises instructions that when executed
by the one or more computer processors cause the one or more
computer processors to apply the usage data to the safety model
that is constructed from training data of known safety events
associated with a plurality of SRLs.
14. The SRL of claim 9, wherein to perform the at least one
operation the memory comprises instructions that when executed by
the one or more computer processors cause the one or more computer
processors to generate at least one alert for output.
15. The SRL of claim 9, wherein to perform the at least one
operation the memory comprises instructions that when executed by
the one or more computer processors cause the one or more computer
processors to send a message to a remote computing device.
15. The SRL of claim 9, wherein the one or more sensors comprise at
least one of an extension sensor, a tension sensor, an
accelerometer, a location sensor, or an altimeter.
16. The SRL of claim 9, further comprising one or more environment
sensors configured to generate data indicative of an environment in
which the self-retracting lifeline is located.
17. The SRL of claim 16, wherein the one or more environment
sensors are configured to generate data indicative of at least one
of temperature, barometric pressure, humidity, particulate content,
or ambient noise.
18. A computing device comprising: one or more computer processors;
and a memory comprising instructions that when executed by the one
or more computer processors cause the one or more computer
processors to: receive, from one or more electronic sensors
configured in a self-retracting lifeline (SRL), usage data for the
SRL; apply the usage data to a safety model stored in the memory,
the usage data characterizing a force applied to a lifeline of the
SRL based on activity of a user of the SRL; determine, based on
identification of anomalous behavior of the user of the SRL
relative to known safe behavior characterized by the safety model,
an occurrence of a safety event associated with the SRL based on
application of the usage data to the safety model, wherein the
anomalous behavior of the user is characterized by the force
applied to the lifeline of the SRL over a time period exceeding a
force associated with safe activity of the SRL over the time
period; and perform at least one operation based at least in part
on determining the occurrence of the safety event associated with
the SRL.
19. The computing device of claim 18, wherein the safety model is
constructed from historical data of known safety events from a
plurality of SRLs having similar characteristics to the SRL.
20. The computing device of claim 18, wherein to perform the at
least one operation the memory comprises instructions that when
executed by the one or more computer processors cause the one or
more computer processors to generate at least one alert for
output.
21. The computing device of claim 18, wherein to perform the at
least one operation the memory comprises instructions that when
executed by the one or more computer processors cause the one or
more computer processors to send a message to a remote computing
device.
Description
TECHNICAL FIELD
[0001] This disclosure relates to safety equipment and, in
particular, fall protection equipment.
BACKGROUND
[0002] Fall protection equipment is important safety equipment for
workers operating at potentially harmful or even deadly heights.
For example, to help ensure safety in the event of a fall, workers
often wear safety harnesses connected to support structures with
fall protection equipment such as lanyards, energy absorbers,
self-retracting lifelines (SRLs), descenders, and the like. An SRL
typically includes a lifeline that is wound about a biased drum
rotatably connected to a housing. Movement of the lifeline causes
the drum to rotate as the lifeline is extended out from and
retracted into the housing. Examples of self-retracting lifelines
include the ULTRA-LOK self-retracting lifeline, the NANO-LOK
self-retracting lifeline, and the REBEL self-retracting lifeline
manufactured by 3M Fall Protection Business.
SUMMARY
[0003] In general, this disclosure describes techniques for
monitoring and predicting safety events for fall protection
equipment, such as SRLs. In some examples, a safety event may refer
to activities of a user of personal protective equipment (PPE), a
condition of the PPE, or a hazardous environmental condition. For
example, in the context of fall protection equipment, a safety
event may be misuse of the fall protection equipment, a user of the
fall equipment experiencing a fall, or a failure of the fall
protection equipment. In the context of a respirator, a safety
event may be misuse of the respirator, a user of the respirator not
receiving an appropriate quality and/or quantity of air, or failure
of the respirator. A safety event may also be associated with a
hazard in the environment in which the PPE is located.
[0004] According to aspects of this disclosure, SRLs may be
configured to incorporate one or more electronic sensors for
capturing data that is indicative of operation of the SRL, location
of the SRL, or environmental conditions surrounding the SRL. In
some instances, the electronic sensors may be configured to measure
length, speed, acceleration, force, or a variety of other
characteristics associated with a lifeline of an SRL, the location
of the SRL, and/or environmental factors associated with an
environment in which the SRL is located, generally referred to
herein as usage data or acquired sensor data. SRLs may be
configured to transmit the usage data to a management system
configured to execute an analytics engine that applies the usage
data (or at least a subset of the usage data) to a safety model to
predict a likelihood of an occurrence of a safety event associated
with an SRL in real-time or near real-time as a user (e.g., a
worker) engages in activities while wearing the SRL. In this way,
the techniques may provide tools to accurately measure and/or
monitor operation of an SRL, determine predictive outcomes based on
the operation and generate alerts, models or rule sets that may be
employed to warn the potential of or even avoid, in real-time or
pseudo real-time, imminent safety events.
[0005] In some examples, a system includes: a self-retracting
lifeline (SRL) comprising one or more electronic sensors, the one
or more electronic sensors configured to generate data that is
indicative of an operation of the SRL; and at least one computing
device comprising one or more computer processors and a memory
comprising instructions that when executed by the one or more
computer processors cause the one or more computer processors to:
receive the data that is indicative of the operation of the SRL;
apply the data to a safety model that predicts a likelihood of an
occurrence of a safety event associated with the SRL; and perform
one or more operations based at least in part on the likelihood of
the occurrence of the safety event.
[0006] In some examples, a self-retracting lifeline includes: a
first connector configured to be coupled to an anchor; a second
connector configured to be coupled to a user of the lifeline; and
one or more electronic sensors, the one or more electronic sensors
configured to generate usage data that is indicative of an
operation of the SRL.
[0007] In some examples, a computing device includes: one or more
computer processors; and a memory comprising instructions that when
executed by the one or more computer processors cause the one or
more computer processors to: obtain usage data from at least one
self-retracting lifeline (SRL), wherein the usage data comprises
data indicative of operation of the at least one SRL; apply the
usage data to a safety model that characterizes activity of a user
of the at least one SRL; predict a likelihood of an occurrence of a
safety event associated with the at least one SRL based on
application of the usage data to the safety model; and generate an
output in response to predicting the likelihood of the occurrence
of the safety event.
[0008] The details of one or more examples of the disclosure are
set forth in the accompanying drawings and the description below.
Other features, objects, and advantages of the disclosure will be
apparent from the description and drawings, and from the
claims.
BRIEF DESCRIPTION OF DRAWINGS
[0009] FIG. 1 is a block diagram illustrating an example system in
which personal protection equipment (PPEs) having embedded sensors
and communication capabilities are utilized within a number of work
environments and are managed by a personal protection equipment
management system in accordance with various techniques of this
disclosure.
[0010] FIG. 2 is a block diagram illustrating an operating
perspective of the personal protection equipment management system
shown in FIG. 1.
[0011] FIG. 3 is a block diagram illustrating one example of a
self-retracting lifeline (SRL), in accordance with aspects of this
disclosure.
[0012] FIG. 4A illustrates an example of an encoder that may be
included in an SRL, in accordance with aspects of this
disclosure.
[0013] FIG. 4B illustrates and example of a deflector unit that may
be included in an SRL, in accordance with aspects of this
disclosure.
[0014] FIG. 5 is a conceptual diagram illustrating an example of an
SRL in communication with a wearable data hub, in accordance with
various aspects of this disclosure.
[0015] FIG. 6 illustrates an example model applied by the personal
protection equipment management system or other devices herein with
respect to worker activity in terms of measure line speed,
acceleration and line length, where the model is arranged to define
safe regions and regions unsafe behavior predictive of safety
events, in accordance with aspects of this disclosure.
[0016] FIG. 7 illustrates an example of a second model applied by
the personal protection equipment management system or other
devices herein with respect to worker activity in terms of measure
force/tension on the safety line and length, where the model is
arranged to define a safe region and regions unsafe behavior
predictive of safety events, in accordance with aspects of this
disclosure.
[0017] FIGS. 8A and 8B illustrate profiles of example usage data
from workers determined by the personal protection equipment
management system to represent low risk behavior and high risk
behavior triggering alerts or other responses, in accordance with
aspects of this disclosure.
[0018] FIG. 9 is a flow diagram illustrating an example process for
predicting the likelihood of a safety event, according to aspects
of this disclosure.
DETAILED DESCRIPTION
[0019] According to aspects of this disclosure, an SRL may be
configured to incorporate one or more electronic sensors for
capturing data that is indicative of operation, location, or
environmental conditions surrounding the SRL. Such data may
generally be referred to herein as usage data or, alternatively,
sensor data. Usage data may take the form of a stream of samples
over a period of time. In some instances, the electronic sensors
may be configured to measure length, speed, acceleration, force, or
a variety of other characteristics associated with a lifeline of an
SRL, positional information indicative of the location of the SRL,
and/or environmental factors associated with an environment in
which the SRL is located. Moreover, as described herein, an SRL may
be configured to include one or more electronic components for
outputting communication to the respective worker, such as
speakers, vibration devices, LEDs, buzzers or other devices for
outputting alerts, audio messages, sounds, indicators and the
like.
[0020] According to aspects of this disclosure, SRLs may be
configured to transmit the acquired usage data to a personal
protection equipment management system (PPEMS), which may be a
cloud-based system having an analytics engine configured to process
streams of incoming usage data from SRLs or other personal
protection equipment deployed and used by a population of workers
at various work environments. The analytics engine of the PPEMS may
apply one or more models to the streams of incoming usage data (or
at least a subset of the usage data) to monitor and predict the
likelihood of an occurrence of a safety event for the worker
associated with any individual SRL. For example, the analytics
engine may compare measured parameters (e.g., as measured by the
electronic sensors) to known models that characterize activity of a
user of an SRL, e.g., that represent safe activities, unsafe
activities, or activities of concern (which may typically occur
prior to unsafe activities) in order to determine the probability
of an event occurring.
[0021] The analytics engine then may generate an output in response
to predicting the likelihood of the occurrence of a safety event.
For example, the analytics engine may generate an output that
indicates a safety event is likely to occur based on data collected
from a user of an SRL. The output may be used to alert the user of
the SRL that a safety event is likely to occur, allowing the user
to modify or adjust their behavior. In other examples, circuitry
embedded within the SRLs or processors within intermediate data
hubs more local to the workers may be programmed via the PPEMS or
other mechanism to apply models or rule sets determined by the
PPEMS so as to locally generate and output alerts or other
preventative measure designed to avoid or mitigate a predicted
safety event. In this way, the techniques provide tools to
accurately measure and/or monitor operation of an SRL and determine
predictive outcomes based on the operation.
[0022] FIG. 1 is a block diagram illustrating an example computing
system 2 that includes a personal protection equipment management
system (PPEMS) 6 for managing personal protection equipment. As
described herein, PPEMS allows authorized users to perform
preventive occupational health and safety actions and manage
inspections and maintenance of safety protective equipment. By
interacting with PPEMS 6, safety professionals can, for example,
manage area inspections, worker inspections, worker health and
safety compliance training.
[0023] In general, PPEMS 6 provides data acquisition, monitoring,
activity logging, reporting, predictive analytics and alert
generation. For example, PPEMS 6 includes an underlying analytics
and safety event prediction engine and alerting system in
accordance with various examples described herein. As further
described below, PPEMS 6 provides an integrated suite of personal
safety protection equipment management tools and implements various
techniques of this disclosure. That is, PPEMS 6 provides an
integrated, end-to-end system for managing personal protection
equipment, e.g., safety equipment, used by workers 8 within one or
more physical environments 10, which may be construction sites,
mining or manufacturing sites or any physical environment. The
techniques of this disclosure may be realized within various parts
of computing environment 2.
[0024] As shown in the example of FIG. 1, system 2 represents a
computing environment in which a computing device within of a
plurality of physical environments 8A, 8B (collectively,
environments 8) electronically communicate with PPEMS 6 via one or
more computer networks 4. Each of physical environment 8 represents
a physical environment, such as a work environment, in which one or
more individuals, such as workers 10, utilize personal protection
equipment while engaging in tasks or activities within the
respective environment.
[0025] In this example, environment 8A is shown as generally as
having workers 10, while environment 8B is shown in expanded form
to provide a more detailed example. In the example of FIG. 1, a
plurality of workers 10A-10N are shown as utilizing respective fall
protection equipment, which are shown in this example as
self-retracting lifelines (SRLs) 11A-11N, attached to safety
support structure 12.
[0026] As further described herein, each of SRLs 11 includes
embedded sensors or monitoring devices and processing electronics
configured to capture data in real-time as a user (e.g., worker)
engages in activities while wearing the fall protection equipment.
For example, as described in greater detail with respect to the
example shown in FIG. 3, SRLs may include a variety of electronic
sensors such as one or more of an extension sensor, a tension
sensor, an accelerometer, a location sensor, an altimeter, one or
more environment sensors, and/or other sensors for measuring
operations of SRLs 11. In addition, each of SRLs 11 may include one
or more output devices for outputting data that is indicative of
operation of SRLs 11 and/or generating and outputting
communications to the respective worker 10. For example, SRLs 11
may include one or more devices to generate audible feedback (e.g.,
one or more speakers), visual feedback (e.g., one or more displays,
light emitting diodes (LEDs) or the like), or tactile feedback
(e.g., a device that vibrates or provides other haptic
feedback).
[0027] In general, each of environments 8 include computing
facilities (e.g., a local area network) by which SRLs 11 are able
to communicate with PPEMS 6. For examples, environments 8 may be
configured with wireless technology, such as 802.11 wireless
networks, 802.15 ZigBee networks, and the like. In the example of
FIG. 1, environment 8B includes a local network 7 that provides a
packet-based transport medium for communicating with PPEMS 6 via
network 4. In addition, environment 8B includes a plurality of
wireless access points 19A, 19B that may be geographically
distributed throughout the environment to provide support for
wireless communications throughout the work environment.
[0028] Each of SRLs 11 is configured to communicate data, such as
sensed motions, events and conditions, via wireless communications,
such as via 802.11 WiFi protocols, Bluetooth protocol or the like.
SRLs 11 may, for example, communicate directly with a wireless
access point 19. As another example, each worker 10 may be equipped
with a respective one of wearable communication hubs 14A-14M that
enable and facilitate communication between SRLs 11 and PPEMS 6. In
some examples, a hub may be an intrinsically safe computing device,
smartphone, wrist-, head-, or body-worn computing device, or any
other computing device. SRLs 11 as well as other PPEs for the
respective worker 11 may communicate with a respective
communication hub 14 via Bluetooth or other short range protocol,
and the communication hubs may communicate with PPEMs 6 via
wireless communications processed by wireless access points 19.
Although shown as wearable devices, hubs 14 may be implemented as
stand-alone devices deployed within environment 8B. In some
examples, a hub may be an article of PPE.
[0029] In general, each of hubs 14 operates as a wireless device
for SRLs 11 relaying communications to and from SRLs 11, and may be
capable of buffering usage data in case communication is lost with
PPEMS 6. Moreover, each of hubs 14 is programmable via PPEMS 6 so
that local alert rules may be installed and executed without
requiring a connection to the cloud. As such, each of hubs 14
provides a relay of streams of usage data from SRLs 11 and/or other
PPEs within the respective environment, and provides a local
computing environment for localized alerting based on streams of
events in the event communication with PPEMS 6 is lost.
[0030] As shown in the example of FIG. 1, an environment, such as
environment 8B, may also one or more wireless-enabled beacons, such
as beacons 17A-17C, that provide accurate location information
within the work environment. For example, beacons 17A-17C may be
GPS-enabled such that a controller within the respective beacon may
be able to precisely determine the position of the respective
beacon. Based on wireless communications with one or more of
beacons 17, a given SRL 11 or communication hub 14 worn by a worker
10 is configured to determine the location of the worker within
work environment 8B. In this way, event data reported to PPEMS 6
may be stamped with positional information to aid analysis,
reporting and analytics performed by the PPEMS.
[0031] In addition, an environment, such as environment 8B, may
also one or more wireless-enabled sensing stations, such as sensing
stations 21A, 21B. Each sensing station 21 includes one or more
sensors and a controller configured to output data indicative of
sensed environmental conditions. Moreover, sensing stations 21 may
be positioned within respective geographic regions of environment
8B or otherwise interact with beacons 17 to determine respective
positions and include such positional information when reporting
environmental data to PPEMS 6. As such, PPEMS 6 may configured to
correlate the senses environmental conditions with the particular
regions and, therefore, may utilize the captured environmental data
when processing event data received from SRLs 11. For example,
PPEMS 6 may utilize the environmental data to aid generating alerts
or other instructions for SRLs 11 and for performing predictive
analytics, such as determining any correlations between certain
environmental conditions (e.g., heat, humidity, visibility) with
abnormal worker behavior or increased safety events. As such, PPEMS
6 may utilize current environmental conditions to aid prediction
and avoidance of imminent safety events. Example environmental
conditions that may be sensed by sensing devices 21 include but are
not limited to temperature, humidity, presence of gas, pressure,
visibility, wind and the like.
[0032] In example implementations, an environment, such as
environment 8B, may also include one or more safety stations 15
distributed throughout the environment to provide viewing stations
for accessing PPEMs 6. Safety stations 15 may allow one of workers
10 to check out SRLs 11 and/or other safety equipment, verify that
safety equipment is appropriate for a particular one of
environments 8, and/or exchange data. For example, safety stations
15 may transmit alert rules, software updates, or firmware updates
to SRLs 11 or other equipment. Safety stations 15 may also receive
data cached on SRLs 11, hubs 14, and/or other safety equipment.
That is, while SRLs 11 (and/or data hubs 14) may typically transmit
usage data from sensors of SRLs 11 to network 4, in some instances,
SRLs 11 (and/or data hubs 14) may not have connectivity to network
4. In such instances, SRLs 11 (and/or data hubs 14) may store usage
data locally and transmit the usage data to safety stations 15 upon
being in proximity with safety stations 15. Safety stations 15 may
then upload the data from SRLs 11 and connect to network 4.
[0033] In addition, each of environments 8 include computing
facilities that provide an operating environment for end-user
computing devices 16 for interacting with PPEMS 6 via network 4.
For example, each of environments 8 typically includes one or more
safety managers responsible for overseeing safety compliance within
the environment. In general, each user 20 interacts with computing
devices 16 to access PPEMS 6. Each of environments 8 may include
systems. Similarly, remote users may use computing devices 18 to
interact with PPEMS via network 4. For purposes of example, the
end-user computing devices 16 may be laptops, desktop computers,
mobile devices such as tablets or so-called smart phones and the
like.
[0034] Users 20, 24 interact with PPEMS 6 to control and actively
manage many aspects of safely equipment utilized by workers 10,
such as accessing and viewing usage records, analytics and
reporting. For example, users 20, 24 may review usage information
acquired and stored by PPEMS 6, where the usage information may
include data specifying starting and ending times over a time
duration (e.g., a day, a week, or the like), data collected during
particular events, such as detected falls, sensed data acquired
from the user, environment data, and the like. In addition, users
20, 24 may interact with PPEMS 6 to perform asset tracking and to
schedule maintenance events for individual pieces of safety
equipment, e.g., SRLs 11, to ensure compliance with any procedures
or regulations. PPEMS 6 may allow users 20, 24 to create and
complete digital checklists with respect to the maintenance
procedures and to synchronize any results of the procedures from
computing devices 16, 18 to PPEMS 6.
[0035] Further, as described herein, PPEMS 6 integrates an event
processing platform configured to process thousand or even millions
of concurrent streams of events from digitally enabled PPEs, such
as SRLs 11. An underlying analytics engine of PPEMS 6 applies
historical data and models to the inbound streams to compute
assertions, such as identified anomalies or predicted occurrences
of safety events based on conditions or behavior patterns of
workers 11. Further, PPEMS 6 provides real-time alerting and
reporting to notify workers 10 and/or users 20, 24 of any predicted
events, anomalies, trends, and the like.
[0036] The analytics engine of PPEMS 6 may, in some examples, apply
analytics to identify relationships or correlations between sensed
worker data, environmental conditions, geographic regions and other
factors and analyze the impact on safety events. PPEMS 6 may
determine, based on the data acquired across populations of workers
10, which particular activities, possibly within certain geographic
region, lead to, or are predicted to lead to, unusually high
occurrences of safety events.
[0037] In this way, PPEMS 6 tightly integrates comprehensive tools
for managing personal protection equipment with an underlying
analytics engine and communication system to provide data
acquisition, monitoring, activity logging, reporting, behavior
analytics and alert generation. Moreover, PPEMS 6 provides a
communication system for operation and utilization by and between
the various elements of system 2. Users 20, 24 may access PPEMS to
view results on any analytics performed by PPEMS 6 on data acquired
from workers 10. In some examples, PPEMS 6 may present a web-based
interface via a web server (e.g., an HTTP server) or client-side
applications may be deployed for devices of computing devices 16,
18 used by users 20, 24, such as desktop computers, laptop
computers, mobile devices such as smartphones and tablets, or the
like.
[0038] In some examples, PPEMS 6 may provide a database query
engine for directly querying PPEMS 6 to view acquired safety
information, compliance information and any results of the analytic
engine, e.g., by the way of dashboards, alert notifications,
reports and the like. That is, users 24, 26, or software executing
on computing devices 16, 18, may submit queries to PPEMS 6 and
receive data corresponding to the queries for presentation in the
form of one or more reports or dashboards. Such dashboards may
provide various insights regarding system 2, such as baseline
("normal") operation across worker populations, identifications of
any anomalous workers engaging in abnormal activities that may
potentially expose the worker to risks, identifications of any
geographic regions within environments 2 for which unusually
anomalous (e.g., high) safety events have been or are predicted to
occur, identifications of any of environments 2 exhibiting
anomalous occurrences of safety events relative to other
environments, and the like.
[0039] As illustrated in detail below, PPEMS 6 may simplify
workflows for individuals charged with monitoring and ensure safety
compliance for an entity or environment. That is, the techniques of
this disclosure may enable active safety management and allow an
organization to take preventative or correction actions with
respect to certain regions within environments 8, particular pieces
of safety equipment 11 or individual workers 10, define and may
further allow the entity to implement workflow procedures that are
data-driven by an underlying analytical engine.
[0040] As one example, the underlying analytical engine of PPEMS 6
may be configured to compute and present customer-defined metrics
for worker populations within a given environment 8 or across
multiple environments for an organization as a whole. For example,
PPEMS 6 may be configured to acquire data and provide aggregated
performance metrics and predicted behavior analytics across a
worker population (e.g., across workers 10 of either or both of
environments 8A, 8B). Furthermore, users 20, 24 may set benchmarks
for occurrence of any safety incidences, and PPEMS 6 may track
actual performance metrics relative to the benchmarks for
individuals or defined worker populations.
[0041] As another example, PPEMS 6 may further trigger an alert if
certain combinations of conditions are present, e.g., to accelerate
examination or service of a safety equipment, such as one of SRLs
11. In this manner, PPEMS 6 may identify individual pieces of SRLs
11 or workers 10 for which the metrics do not meet the benchmarks
and prompt the users to intervene and/or perform procedures to
improve the metrics relative to the benchmarks, thereby ensuring
compliance and actively managing safety for workers 10. In some
examples, one or more operations may include changing the operation
of one or more articles of PPE including but not limited to SRLs
11.
[0042] FIG. 2 is a block diagram providing an operating perspective
of PPEMS 6 when hosted as cloud-based platform capable of
supporting multiple, distinct work environments 8 having an overall
population of workers 10 that have a variety of communication
enabled personal protection equipment (PPE), such as safety release
lines (SRLs) 11, respirators 13, safety helmets or other safety
equipment. In the example of FIG. 2, the components of PPEMS 6 are
arranged according to multiple logical layers that implement the
techniques of the disclosure. Each layer may be implemented by a
one or more modules comprised of hardware, software, or a
combination of hardware and software.
[0043] In FIG. 2, personal protection equipment (PPEs) 62, such as
SRLs 11, respirators 13 and/or other equipment, either directly or
by way of HUBs 14, as well as computing devices 60, operate as
clients 63 that communicate with PPEMS 6 via interface layer 64.
Computing devices 60 typically execute client software
applications, such as desktop applications, mobile application, and
web applications. Computing devices 60 may represent any of
computing devices 16, 18 of FIG. 1. Examples of computing devices
60 may include, but are not limited to a portable or mobile
computing device (e.g., smartphone, wearable computing device,
tablet), laptop computers, desktop computers, smart television
platforms, and servers, to name only a few examples.
[0044] As further described in this disclosure, PPEs 62 communicate
with PPEMS 6 (directly or via hubs 14) to provide streams of data
acquired from embedded sensors and other monitoring circuitry and
receive from PPEMS 6 alerts, configuration and other
communications. Client applications executing on computing devices
60 may communicate with PPEMS 6 to send and receive information
that is retrieved, stored, generated, and/or otherwise processed by
services 68. For instance, the client applications may request and
edit safety event information including analytical data stored at
and/or managed by PPEMS 6. In some examples, client applications 61
may request and display aggregate safety event information that
summarizes or otherwise aggregates numerous individual instances of
safety events and corresponding data acquired from PPEs 62 and or
generated by PPEMS 6. The client applications may interact with
PPEMS 6 to query for analytics information about past and predicted
safety events, behavior trends of workers 10, to name only a few
examples. In some examples, the client applications may output for
display information received from PPEMS 6 to visualize such
information for users of clients 63. As further illustrated and
described in below, PPEMS 6 may provide information to the client
applications, which the client applications output for display in
user interfaces.
[0045] Clients applications executing on computing devices 60 may
be implemented for different platforms but include similar or the
same functionality. For instance, a client application may be a
desktop application compiled to run on a desktop operating system,
such as Microsoft Windows, Apple OS X, or Linux, to name only a few
examples. As another example, a client application may be a mobile
application compiled to run on a mobile operating system, such as
Google Android, Apple iOS, Microsoft Windows Mobile, or BlackBerry
OS to name only a few examples. As another example, a client
application may be a web application such as a web browser that
displays web pages received from PPEMS 6. In the example of a web
application, PPEMS 6 may receive requests from the web application
(e.g., the web browser), process the requests, and send one or more
responses back to the web application. In this way, the collection
of web pages, the client-side processing web application, and the
server-side processing performed by PPEMS 6 collectively provides
the functionality to perform techniques of this disclosure. In this
way, client applications use various services of PPEMS 6 in
accordance with techniques of this disclosure, and the applications
may operate within various different computing environment (e.g.,
embedded circuitry or processor of a PPE, a desktop operating
system, mobile operating system, or web browser, to name only a few
examples).
[0046] As shown in FIG. 2, PPEMS 6 includes an interface layer 64
that represents a set of application programming interfaces (API)
or protocol interface presented and supported by PPEMS 6. Interface
layer 64 initially receives messages from any of clients 63 for
further processing at PPEMS 6. Interface layer 64 may therefore
provide one or more interfaces that are available to client
applications executing on clients 63. In some examples, the
interfaces may be application programming interfaces (APIs) that
are accessible over a network. Interface layer 64 may be
implemented with one or more web servers. The one or more web
servers may receive incoming requests, process and/or forward
information from the requests to services 68, and provide one or
more responses, based on information received from services 68, to
the client application that initially sent the request. In some
examples, the one or more web servers that implement interface
layer 64 may include a runtime environment to deploy program logic
that provides the one or more interfaces. As further described
below, each service may provide a group of one or more interfaces
that are accessible via interface layer 64.
[0047] In some examples, interface layer 64 may provide
Representational State Transfer (RESTful) interfaces that use HTTP
methods to interact with services and manipulate resources of PPEMS
6. In such examples, services 68 may generate JavaScript Object
Notation (JSON) messages that interface layer 64 sends back to the
client application 61 that submitted the initial request. In some
examples, interface layer 64 provides web services using Simple
Object Access Protocol (SOAP) to process requests from client
applications 61. In still other examples, interface layer 64 may
use Remote Procedure Calls (RPC) to process requests from clients
63. Upon receiving a request from a client application to use one
or more services 68, interface layer 64 sends the information to
application layer 66, which includes services 68.
[0048] As shown in FIG. 2, PPEMS 6 also includes an application
layer 66 that represents a collection of services for implementing
much of the underlying operations of PPEMS 6. Application layer 66
receives information included in requests received from client
applications 61 and further processes the information according to
one or more of services 68 invoked by the requests. Application
layer 66 may be implemented as one or more discrete software
services executing on one or more application servers, e.g.,
physical or virtual machines. That is, the application servers
provide runtime environments for execution of services 68. In some
examples, the functionality interface layer 64 as described above
and the functionality of application layer 66 may be implemented at
the same server.
[0049] Application layer 66 may include one or more separate
software services 68, e.g., processes that communicate, e.g., via a
logical service bus 70 as one example. Service bus 70 generally
represents a logical interconnections or set of interfaces that
allows different services to send messages to other services, such
as by a publish/subscription communication model. For instance,
each of services 68 may subscribe to specific types of messages
based on criteria set for the respective service. When a service
publishes a message of a particular type on service bus 70, other
services that subscribe to messages of that type will receive the
message. In this way, each of services 68 may communicate
information to one another. As another example, services 68 may
communicate in point-to-point fashion using sockets or other
communication mechanism. In still other examples, a pipeline system
architecture could be used to enforce a workflow and logical
processing of data a messages as they are process by the software
system services. Before describing the functionality of each of
services 68, the layers is briefly described herein.
[0050] Data layer 72 of PPEMS 6 represents a data repository that
provides persistence for information in PPEMS 6 using one or more
data repositories 74. A data repository, generally, may be any data
structure or software that stores and/or manages data. Examples of
data repositories include but are not limited to relational
databases, multi-dimensional databases, maps, and hash tables, to
name only a few examples. Data layer 72 may be implemented using
Relational Database Management System (RDBMS) software to manage
information in data repositories 74. The RDBMS software may manage
one or more data repositories 74, which may be accessed using
Structured Query Language (SQL). Information in the one or more
databases may be stored, retrieved, and modified using the RDBMS
software. In some examples, data layer 72 may be implemented using
an Object Database Management System (ODBMS), Online Analytical
Processing (OLAP) database or other suitable data management
system.
[0051] As shown in FIG. 2, each of services 68A-68H ("services 68")
is implemented in a modular form within PPEMS 6. Although shown as
separate modules for each service, in some examples the
functionality of two or more services may be combined into a single
module or component. Each of services 68 may be implemented in
software, hardware, or a combination of hardware and software.
Moreover, services 68 may be implemented as standalone devices,
separate virtual machines or containers, processes, threads or
software instructions generally for execution on one or more
physical processors.
[0052] In some examples, one or more of services 68 may each
provide one or more interfaces that are exposed through interface
layer 64. Accordingly, client applications of computing devices 60
may call one or more interfaces of one or more of services 68 to
perform techniques of this disclosure.
[0053] In accordance with techniques of the disclosure, services 68
may include an event processing platform including an event
endpoint frontend 68A, event selector 68B, event processor 68C and
high priority (HP) event processor 68D. Event endpoint frontend 68A
operates as a front end interface for receiving and sending
communications to PPEs 62 and hubs 14. In other words, event
endpoint frontend 68A operates to as a front line interface to
safety equipment deployed within environments 8 and utilized by
workers 10. In some instances, event endpoint frontend 68A may be
implemented as a plurality of tasks or jobs spawned to receive
individual inbound communications of event streams 69 from the PPEs
62 carrying data sensed and captured by the safety equipment. When
receiving event streams 69, for example, event endpoint frontend
68A may spawn tasks to quickly enqueue an inbound communication,
referred to as an event, and close the communication session,
thereby providing high-speed processing and scalability. Each
incoming communication may, for example, carry data recently
captured data representing sensed conditions, motions,
temperatures, actions or other data, generally referred to as
events. Communications exchanged between the event endpoint
frontend 68A and the PPEs may be real-time or pseudo real-time
depending on communication delays and continuity.
[0054] Event selector 68B operates on the stream of events 69
received from PPEs 62 and/or hubs 14 via frontend 68A and
determines, based on rules or classifications, priorities
associated with the incoming events. Based on the priorities, event
selector 68B enqueues the events for subsequent processing by event
processor 68C or high priority (HP) event processor 68D. Additional
computational resources and objects may be dedicated to HP event
processor 68D so as to ensure responsiveness to critical events,
such as incorrect usage of PPEs, use of incorrect filters and/or
respirators based on geographic locations and conditions, failure
to properly secure SRLs 11 and the like. Responsive to processing
high priority events, HP event processor 68D may immediately invoke
notification service 68E to generate alerts, instructions, warnings
or other similar messages to be output to SRLs 11, hubs 14 and/or
remote users 20, 24. Events not classified as high priority are
consumed and processed by event processor 68C.
[0055] In general, event processor 68C or high priority (HP) event
processor 68D operate on the incoming streams of events to update
event data 74A within data repositories 74. In general, event data
74A may include all or a subset of usage data obtained from PPEs
62. For example, in some instances, event data 74A may include
entire streams of samples of data obtained from electronic sensors
of PPEs 62. In other instances, event data 74A may include a subset
of such data, e.g., associated with a particular time period or
activity of PPEs 62. Event processors 68C, 68D may create, read,
update, and delete event information stored in event data 74A.
Event information for may be stored in a respective database record
as a structure that includes name/value pairs of information, such
as data tables specified in row/column format. For instance, a name
(e.g., column) may be "worker ID" and a value may be an employee
identification number. An event record may include information such
as, but not limited to: worker identification, PPE identification,
acquisition timestamp(s) and data indicative of one or more sensed
parameters.
[0056] In addition, event selector 68B directs the incoming stream
of events to stream analytics service 68F, which represents an
example of an analytics engine configured to perform in depth
processing of the incoming stream of events to perform real-time
analytics. Stream analytics service 68F may, for example, be
configured to process and compare multiple streams of event data
74A with historical data and models 74B in real-time as event data
74A is received. In this way, stream analytic service 68D may be
configured to detect anomalies, transform incoming event data
values, trigger alerts upon detecting safety concerns based on
conditions or worker behaviors. Historical data and models 74B may
include, for example, specified safety rules, business rules and
the like. In this way, historical data and models 74B may
characterize activity of a user of SRL 11, e.g., as conforming to
the safety rules, business rules, and the like. In addition, stream
analytic service 68D may generate output for communicating to PPPEs
62 by notification service 68F or computing devices 60 by way of
record management and reporting service 68D.
[0057] In this way, analytics service 68F processes inbound streams
of events, potentially hundreds or thousands of streams of events,
from enabled safety PPEs 62 utilized by workers 10 within
environments 8 to apply historical data and models 74B to compute
assertions, such as identified anomalies or predicted occurrences
of imminent safety events based on conditions or behavior patterns
of the workers. Analytics service may 68D publish the assertions to
notification service 68F and/or record management by service bus 70
for output to any of clients 63.
[0058] In this way, analytics service 68F may configured as an
active safety management system that predicts imminent safety
concerns and provides real-time alerting and reporting. In
addition, analytics service 68F may be a decision support system
that provides techniques for processing inbound streams of event
data to generate assertions in the form of statistics, conclusions,
and/or recommendations on an aggregate or individualized worker
and/or PPE basis for enterprises, safety officers and other remote
users. For instance, analytics service 68F may apply historical
data and models 74B to determine, for a particular worker, the
likelihood that a safety event is imminent for the worker based on
detected behavior or activity patterns, environmental conditions
and geographic locations. In some examples, analytics service 68F
may determine whether a worker is currently impaired, e.g., due to
exhaustion, sickness or alcohol/drug use, and may require
intervention to prevent safety events. As yet another example,
analytics service 68F may provide comparative ratings of workers or
type of safety equipment in a particular environment 8.
[0059] Hence, analytics service 68F may maintain or otherwise use
one or more models that provide risk metrics to predict safety
events. Analytics service 68F may also generate order sets,
recommendations, and quality measures. In some examples, analytics
service 68F may generate user interfaces based on processing
information stored by PPEMS 6 to provide actionable information to
any of clients 63. For example, analytics service 68F may generate
dashboards, alert notifications, reports and the like for output at
any of clients 63. Such information may provide various insights
regarding baseline ("normal") operation across worker populations,
identifications of any anomalous workers engaging in abnormal
activities that may potentially expose the worker to risks,
identifications of any geographic regions within environments for
which unusually anomalous (e.g., high) safety events have been or
are predicted to occur, identifications of any of environments
exhibiting anomalous occurrences of safety events relative to other
environments, and the like.
[0060] Although other technologies can be used, in one example
implementation, analytics service 68F utilizes machine learning
when operating on streams of safety events so as to perform
real-time analytics. That is, analytics service 68F includes
executable code generated by application of machine learning to
training data of event streams and known safety events to detect
patterns. The executable code may take the form of software
instructions or rule sets and is generally referred to as a model
that can subsequently be applied to event streams 69 for detecting
similar patterns and predicting upcoming events.
[0061] Analytics service 68F may, in some example, generate
separate models for a particular worker, a particular population of
workers, a particular environment, or combinations thereof.
Analytics service 68F may update the models based on usage data
received from PPEs 62. For example, analytics service 68F may
update the models for a particular worker, a particular population
of workers, a particular environment, or combinations thereof based
on data received from PPEs 62.
[0062] Alternatively, or in addition, analytics service 68F may
communicate all or portions of the generated code and/or the
machine learning models to hubs 16 (or PPEs 62) for execution
thereon so as to provide local alerting in near-real time to PPEs.
Example machine learning techniques that may be employed to
generate models 74B can include various learning styles, such as
supervised learning, unsupervised learning, and semi-supervised
learning. Example types of algorithms include Bayesian algorithms,
Clustering algorithms, decision-tree algorithms, regularization
algorithms, regression algorithms, instance-based algorithms,
artificial neural network algorithms, deep learning algorithms,
dimensionality reduction algorithms and the like. Various examples
of specific algorithms include Bayesian Linear Regression, Boosted
Decision Tree Regression, and Neural Network Regression, Back
Propagation Neural Networks, the Apriori algorithm, K-Means
Clustering, k-Nearest Neighbour (kNN), Learning Vector Quantization
(LVQ), Self-Organizing Map (SOM), Locally Weighted Learning (LWL),
Ridge Regression, Least Absolute Shrinkage and Selection Operator
(LASSO), Elastic Net, and Least-Angle Regression (LARS), Principal
Component Analysis (PCA) and Principal Component Regression
(PCR).
[0063] Record management and reporting service 68G processes and
responds to messages and queries received from computing devices 60
via interface layer 64. For example, record management and
reporting service 68G may receive requests from client computing
devices for event data related to individual workers, populations
or sample sets of workers, geographic regions of environments 8 or
environments 8 as a whole, individual or groups/types of PPEs 62.
In response, record management and reporting service 68G accesses
event information based on the request. Upon retrieving the event
data, record management and reporting service 68G constructs an
output response to the client application that initially requested
the information. In some examples, the data may be included in a
document, such as an HTML document, or the data may be encoded in a
JSON format or presented by a dashboard application executing on
the requesting client computing device. For instance, as further
described in this disclosure, example user interfaces that include
the event information are depicted in the figures.
[0064] As additional examples, record management and reporting
service 68G may receive requests to find, analyze, and correlate
PPE event information. For instance, record management and
reporting service 68G may receive a query request from a client
application for event data 74A over a historical time frame, such
as a user can view PPE event information over a period of time
and/or a computing device can analyze the PPE event information
over the period of time.
[0065] In example implementations, services 68 may also include
security service 68H that authenticate and authorize users and
requests with PPEMS 6. Specifically, security service 68H may
receive authentication requests from client applications and/or
other services 68 to access data in data layer 72 and/or perform
processing in application layer 66. An authentication request may
include credentials, such as a username and password. Security
service 68H may query security data 74A to determine whether the
username and password combination is valid. Configuration data 74D
may include security data in the form of authorization credentials,
policies, and any other information for controlling access to PPEMS
6. As described above, security data 74A may include authorization
credentials, such as combinations of valid usernames and passwords
for authorized users of PPEMS 6. Other credentials may include
device identifiers or device profiles that are allowed to access
PPEMS 6.
[0066] Security service 68H may provides audit and logging
functionality for operations performed at PPEMS 6. For instance,
security service 68H may log operations performed by services 68
and/or data accessed by services 68 in data layer 72. Security
service 68H may store audit information such as logged operations,
accessed data, and rule processing results in audit data 74C. In
some examples, security service 68H may generate events in response
to one or more rules being satisfied. Security service 68H may
store data indicating the events in audit data 74C.
[0067] PPEMS 6 may include self-check component 68I, self-check
criteria 74E and work relation data 74F. Self-check criteria 74E
may include one or more self-check criterion. Work relation data
74F may include mappings between data that corresponds to PPE,
workers, and work environments. Work relation data 74F may be any
suitable datastore for storing, retrieving, updating and deleting
data. RMRS 69G may store a mapping between the unique identifier of
worker 10A and a unique device identifier of data hub 14A. Work
relation data store 74F may also map a worker to an environment. In
the example of FIG. 2, self-check component 68I may receive or
otherwise determine data from work relation data 74F for data hub
14A, worker 10A, and/or PPE associated with or assigned to worker
10A. Based on this data, self-check component 68I may select one or
more self-check criteria from self-check criteria 74E. Self-check
component 68I may send the self-check criteria to data hub 14A.
[0068] FIG. 3 illustrates an example of one of SRLs 11 in greater
detail. In this example, SRL 11 includes a first connector 90 for
attachment to an anchor, a lifeline 92, and a second connector 94
for attachment to a user (not shown). SRL 11 also includes housing
96 that houses an energy absorption and/or braking system and
computing device 98. In the illustrated example, computing device
98 includes processors 100, memory 102, communication unit 104, an
extension sensor 106, a tension sensor 108, a speedometer 109, an
accelerometer 110, a location sensor 112, an altimeter 114, one or
more environment sensors 116, and output unit 118.
[0069] It should be understood that the architecture and
arrangement of computing device 98 (and, more broadly, SRL 11)
illustrated in FIG. 3 is shown for exemplary purposes only. In
other examples, SRL 11 and computing device 98 may be configured in
a variety of other ways having additional, fewer, or alternative
components than those shown in FIG. 3. For example, in some
instances, computing device 98 may be configured to include only a
subset of components, such as communication unit 104 and extension
sensor 106. Moreover, while the example of FIG. 3 illustrates
computing device 98 as being integrated with housing 96, the
techniques are not limited to such an arrangement.
[0070] First connector 90 may be anchored to a fixed structure,
such as scaffolding or other support structures. Lifeline 92 may be
wound about a biased drum that is rotatably connected to housing
96. Second connector 94 may be connected to a user (e.g., such as
one of workers 10 (FIG. 1)). Hence, in some examples, first
connector 90 may be configured as an anchor point that is connected
to a support structure, and second connector 94 is configured to
include a hook that is connected to a worker. In other examples,
second connector 94 may be connected to an anchor point, while
first connector 90 may be connected to a worker. As the user
performs activities movement of lifeline 92 causes the drum to
rotate as lifeline 92 is extended out and retracted into housing
96.
[0071] In general, computing device 98 may include a plurality of
sensors that may capture real-time data regarding operation of SRL
11 and/or an environment in which SRL 11 is used. Such data may be
referred to herein as usage data. The sensors may be positioned
within housing 96 and/or may be located at other positions within
SRL 11, such as proximate first connector 90 or second connector
94. Processors 100, in one example, are configured to implement
functionality and/or process instructions for execution within
computing device 98. For example, processors 100 may be capable of
processing instructions stored by memory 102. Processors 100 may
include, for example, microprocessors, digital signal processors
(DSPs), application specific integrated circuits (ASICs),
field-programmable gate array (FPGAs), or equivalent discrete or
integrated logic circuitry.
[0072] Memory 102 may include a computer-readable storage medium or
computer-readable storage device. In some examples, memory 102 may
include one or more of a short-term memory or a long-term memory.
Memory 102 may include, for example, random access memories (RAM),
dynamic random access memories (DRAM), static random access
memories (SRAM), magnetic hard discs, optical discs, flash
memories, or forms of electrically programmable memories (EPROM) or
electrically erasable and programmable memories (EEPROM).
[0073] In some examples, memory 102 may store an operating system
(not shown) or other application that controls the operation of
components of computing device 98. For example, the operating
system may facilitate the communication of data from electronic
sensors (e.g., extension sensor 106, tension sensor 108,
accelerometer 110, location sensor 112, altimeter 114, and/or
environmental sensors 116) to communication unit 104. In some
examples, memory 102 is used to store program instructions for
execution by processors 100. Memory 102 may also be configured to
store information within computing device 98 during operation.
[0074] Computing device 98 may use communication unit 104 to
communicate with external devices via one or more wired or wireless
connections. Communication unit 104 may include various mixers,
filters, amplifiers and other components designed for signal
modulation, as well as one or more antennas and/or other components
designed for transmitting and receiving data. Communication unit
104 may send and receive data to other computing devices using any
one or more suitable data communication techniques. Examples of
such communication techniques may include TCP/IP, Ethernet, Wi-Fi,
Bluetooth, 4G, LTE, to name only a few examples. In some instances,
communication unit 104 may operate in accordance with the Bluetooth
Low Energy (BLU) protocol.
[0075] Extension sensor 106 may be configured to generate and
output data indicative of at least one an extension of lifeline 92
and a retraction of lifeline 92. In some examples, extension sensor
106 may generate data indicative of a length of extension of
lifeline 92 or a length of retraction of lifeline 92. In other
examples, extension sensor 106 may generate data indicative of an
extension or retraction cycle. Extension sensor 106 may include one
or more of a rotary encoder, an optical sensor, a Hall effect
sensor, or another sensor for determining position and/or rotation.
Extension sensor 106 may also include, in some examples, one or
more switches that generate an output that indicates a full
extension or full retraction of lifeline 92.
[0076] Tension sensor 108 may be configured to generate data
indicative of a tension of lifeline 92, e.g., relative to second
connector 90. Tension sensor 108 may include a force transducer
that is placed in-line with lifeline 92 to directly or indirectly
measure tension applied to SRL 11. In some instances, tension
sensor 108 may include a strain gauge to measure static force or
static tension on SRL 11. Tension sensor 108 may additionally or
alternatively include a mechanical switch having a spring-biased
mechanism is used to make or break electrical contacts based on a
predetermined tension applied to SRL 11. In still other examples,
tension sensor 108 may include one or more components for
determining a rotation of friction brake of SRL 11. For example,
the one or more components may include a sensor (e.g. an optical
sensor, a Hall effect sensor, or the like) this is configured to
determine relative motion between two components of a brake during
activation of the braking system.
[0077] Speedometer 109 may be configured to generate data
indicative of a speed of lifeline 92. For example, speedometer 109
may measure extension and/or retraction of lifeline (or receive
such measurement from extension sensor 106) and apply the extension
and/or retraction to a time scale (e.g., divide by time).
Accelerometer 110 may be configured to generate data indicative of
an acceleration of SRL 11 with respect to gravity. Accelerometer
110 may be configured as a single- or multi-axis accelerometer to
determine a magnitude and direction of acceleration, e.g., as a
vector quantity, and may be used to determine orientation,
coordinate acceleration, vibration, shock, and/or falling.
[0078] Location sensor 112 may be configured to generate data
indicative of a location of SRL 11 in one of environments 8.
Location sensor 112 may include a Global Positioning System (GPS)
receiver, componentry to perform triangulation (e.g., using beacons
and/or other fixed communication points), or other sensors to
determine the relative location of SRL 11.
[0079] Altimeter 114 may be configured to generate data indicative
of an altitude of SRL 11 above a fixed level. In some examples,
altimeter 114 may be configured to determine altitude of SRL 11
based on a measurement of atmospheric pressure (e.g., the greater
the altitude, the lower the pressure).
[0080] Environment sensors 116 may be configured to generate data
indicative of a characteristic of an environment, such as
environments 8. In some examples, environment sensors 116 may
include one or more sensors configured to measure temperature,
humidity, particulate content, noise levels, air quality, or any
variety of other characteristics of environments in which SRL 11
may be used.
[0081] Output unit 118 may be configured to output data that is
indicative of operation of SRL 11, e.g., as measured by one or more
sensors of SRL 11 (e.g., such as extension sensor 106, tension
sensor 108, accelerometer 110, location sensor 112, altimeter 114,
and/or environmental sensors 116). Output unit 118 may include
instructions executable by processors 100 of computing device 98 to
generate the data associated with operation of SRL 11. In some
examples, output unit 118 may directly output the data from the one
or more sensors of SRL 11. For example, output unit 118 may
generate one or more messages containing real-time or near
real-time data from one or more sensors of SRL 11 for transmission
to another device via communication unit 104.
[0082] In other examples, output unit 118 (and/or processors 100)
may process data from the one or more sensors and generate messages
that characterize the data from the one or more sensors. For
example, output unit 118 may determine a length of time that SRL 11
is in use, a number of extend and retract cycles of lifeline 92
(e.g., based on data from extension sensor 106), an average rate of
speed of a user during use (e.g., based on data from extension
sensor 106 or location sensor 112), an instantaneous velocity or
acceleration of a user of SRL 11 (e.g., based on data from
accelerometer 110), a number of lock-ups of a brake of lifeline 92
and/or a severity of an impact (e.g., based on data from tension
sensor 108).
[0083] In some examples, output unit 118 may be configured to
transmit the usage data in real-time or near-real time to another
device (e.g., PPEs 62) via communication unit 104. However, in some
instances, communication unit 104 may not be able to communicate
with such devices, e.g., due to an environment in which SRL 11 is
located and/or network outages. In such instances, output unit 118
may cache usage data to memory 102. That is, output unit 118 (or
the sensors themselves) may store usage data to memory 102, which
may allow the usage data to be uploaded to another device upon a
network connection becoming available.
[0084] Output unit 118 may also be configured to generate an
audible, visual, tactile, or other output that is perceptible by a
user of SRL 11. For example, output unit 118 may include one more
user interface devices including, as examples, a variety of lights,
displays, haptic feedback generators, speakers or the like. In one
example, output unit 118 may include one or more light emitting
diodes (LEDs) that are located on SRL 11 and/or included in a
remote device that is in a field of view of a user of SRL 11 (e.g.,
indicator glasses, visor, or the like). In another example, output
unit 118 may include one or more speakers that are located on SRL
11 and/or included in a remote device (e.g., earpiece, headset, or
the like). In still another example, output unit 118 may include a
haptic feedback generator that generates a vibration or other
tactile feedback and that is included on SRL 11 or a remote device
(e.g., a bracelet, a helmet, an earpiece, or the like).
[0085] Output unit 118 may be configured to generate the output
based on operation of SRL 11. For example, output unit 118 may be
configured to generate an output that indicates a status of SRL 11
(e.g. that SRL 11 is operating correctly or needs to be inspected,
repaired, or replaced). As another example, output unit 118 may be
configured to generate an output that indicates that SRL 11 is
appropriate for the environment in which SRL 11 is located. In some
examples, output unit 118 may be configured to generate an output
data that indicates that the environment in which SRL 11 is located
is unsafe (e.g., a temperature, particulate level, location or the
like is potentially dangerous to a worker using SRL 11).
[0086] SRL 11 may, in some examples, be configured to store rules
that characterize a likelihood of a safety event, and output unit
118 may be configured to generate an output based on a comparison
of operation of the SRL 11 (as measured by the sensors) to the
rules. For example, SRL 11 may be configured to store rules to
memory 102 based on the above-described models and/or historical
data from PPEMS 6. Storing and enforcing the rules locally may
allow SRL 11 to determine the likelihood of a safety event with
potentially less latency than if such a determination was made by
PPEMS 6 and/or in instances in which there is no network
connectivity available (such that communication with PPEMS 6 is not
possible). In this example, output unit 118 may be configured to
generate an audible, visual, tactile, or other output that alerts a
worker using SRL 11 of potentially unsafe activities, anomalous
behavior, or the like.
[0087] According to aspects of this disclosure, SRL 11 may receive,
via communication unit 104, alert data, and output unit 118 may
generate an output based on the alert data. For example, SRL 11 may
receive alert data from one of hubs 14, PPEMS 6 (directly or via
one or hubs 14), end-user computing devices 16, remote users using
computing devices 18, safety stations 15, or other computing
devices. In some examples, the alert data may be based on operation
of SRL 11. For example, output unit 118 may receive alert data that
indicates a status of the SRL, that SRL is appropriate for the
environment in which SRL 11 is located, that the environment in
which SRL 11 is located is unsafe, or the like.
[0088] In some examples, additionally or alternatively, SRL 11 may
receive alert data associated with a likelihood of a safety event.
For example, as noted above, PPEMS 6 may, in some examples, apply
historical data and models to usage data from SRL 11 in order to
compute assertions, such as anomalies or predicted occurrences of
imminent safety events based on environmental conditions or
behavior patterns of a worker using SRL 11. That is, PPEMS 6 may
apply analytics to identify relationships or correlations between
sensed data from SRL 11, environmental conditions of environment in
which SRL 11 is located, a geographic region in which SRL 11 is
located, and/or other factors. PPEMS 6 may determine, based on the
data acquired across populations of workers 10, which particular
activities, possibly within certain environment or geographic
region, lead to, or are predicted to lead to, unusually high
occurrences of safety events. SRL 11 may receive alert data from
PPEMS 6 that indicates a relatively high likelihood of a safety
event.
[0089] Output unit 118 may interpret the received alert data and
generate an output (e.g., an audible, visual, or tactile output) to
notify a worker using SRL 11 of the alert condition (e.g., that the
likelihood of a safety event is relatively high, that the
environment is dangerous, that SRL 11 is malfunctioning, that one
or more components of SRL 11 need to be repaired or replaced, or
the like). In some instances, output unit 118 (or processors 100)
may additionally or alternatively interpret alert data to modify
operation or enforce rules of SRL 11 in order to bring operation of
SRL 11 into compliance with desired/less risky behavior. For
example, output unit 118 (or processors 100) may actuate a brake on
lifeline 92 in order to prevent lifeline 92 from extending from
housing 96.
[0090] Hence, according to aspects of this disclosure, usage data
from sensors of SRL 11 (e.g., data from extension sensor 106,
tension sensor 108, accelerometer 110, location sensor 112,
altimeter 114, environmental sensors 116, or other sensors) may be
used in a variety of ways. According to some aspects, usage data
may be used to determine usage statistics. For example, PPEMS 6 may
determine, based on usage data from the sensors, an amount of time
that SRL 11 is in use, a number of extension or retraction cycles
of lifeline 92, an average rate of speed with which lifeline 92 is
extended or retracted during use, an instantaneous velocity or
acceleration with which lifeline 92 is extended or retracted during
use, a number of lock-ups of lifeline 92, a severity of impacts to
lifeline 92, or the like. In other examples, the above-noted usage
statistics may be determined and stored locally (e.g., by SRL 11 or
one of hubs 14).
[0091] According to aspects of this disclosure, PPEMS 6 may use the
usage data to characterize activity of worker 10. For example,
PPEMS 6 may establish patterns of productive and nonproductive time
(e.g., based on operation of SRL 11 and/or movement of worker 10),
categorize worker movements, identify key motions, and/or infer
occurrence of key events. That is, PPEMS 6 may obtain the usage
data, analyze the usage data using services 68 (e.g., by comparing
the usage data to data from known activities/events), and generate
an output based on the analysis.
[0092] In some examples, the usage statistics may be used to
determine when SRL 11 is in need of maintenance or replacement. For
example, PPEMS 6 may compare the usage data to data indicative of
normally operating SRLs 11 in order to identify defects or
anomalies. In other examples, PPEMS 6 may also compare the usage
data to data indicative of a known service life statistics of SRLs
11. The usage statistics may also be used to provide an
understanding how SRLs 11 are used by workers 10 to product
developers in order to improve product designs and performance. In
still other examples, the usage statistics may be used to gathering
human performance metadata to develop product specifications. In
still other examples, the usage statistics may be used as a
competitive benchmarking tool. For example, usage data may be
compared between customers of SRLs 11 to evaluate metrics (e.g.
productivity, compliance, or the like) between entire populations
of workers outfitted with SRLs 11.
[0093] Additionally or alternatively, according to aspects of this
disclosure, usage data from sensors of SRLs 11 may be used to
determine status indications. For example, PPEMS 6 may determine
that worker 10 is connected to or disconnected from SRL 11. PPEMS 6
may also determine an elevation and/or position of worker 10
relative to some datum. PPEMS 6 may also determine that worker 10
is nearing a predetermined length of extraction of lifeline 92.
PPEMS 6 may also determine a proximity of worker 10 to a hazardous
area in one of environments 8 (FIG. 1). In some instances, PPEMS 6
may determine maintenance intervals for SRLs 11 based on use of
SRLs 11 (as indicated by usage data) and/or environmental
conditions of environments in which SRLs 11 are located. PPEMS 6
may also determine, based on usage data, whether SRL 11 is
connected to an anchor/fixed structure and/or whether the
anchor/fixed structure is appropriate.
[0094] Additionally or alternatively, according to aspects of this
disclosure, usage data from sensors of SRLs 11 may be used to
assess performance of worker 10 wearing SRL 11. For example, PPEMS
6 may, based on usage data from SRLs 11, recognize motion that may
indicate a pending fall by worker 10. PPEMS 6 may also, based on
usage data from SRLs 11, to recognize motion that may indicate
fatigue. In some instances, PPEMS 6 may, based on usage data from
SRLs 11, infer that a fall has occurred or that worker 10 is
incapacitated. PPEMS 6 may also perform fall data analysis after a
fall has occurred and/or determine temperature, humidity and other
environmental conditions as they relate to the likelihood of safety
events.
[0095] Additionally or alternatively, according to aspects of this
disclosure, usage data from sensors of SRLs 11 may be used to
determine alerts and/or actively control operation of SRLs 11. For
example, PPEMS 6 may determine that a safety event such as a fall
is imminent and active a brake of SRL 11. In some instances, PPEMS
6 may adjust the performance of the arrest characteristics to the
fall dynamics. That is, PPEMS 6 may alert that control that is
applied to SRL 11 based on the particular characteristics of the
safety event (e.g., as indicated by usage data). PPEMS 6 may
provide, in some examples, a warning when worker 10 is near a
hazard in one of environments 8 (e.g., based on location data
gathered from location sensor 112). PPEMS 6 may also lock out SRL
11 such that SRL 11 will not operate after SRL 11 has experienced
an impact or is in need of service.
[0096] Again, PPEMS 6 may determine the above-described performance
characteristics and/or generate the alert data based on application
of the usage data to one or more safety models that characterizes
activity of a user of SRL 11. The safety models may be trained
based on historical data or known safety events. However, while the
determinations are described with respect to PPEMS 6, as described
in greater detail herein, one or more other computing devices, such
as hubs 14 or SRLs 11 may be configured to perform all or a subset
of such functionality.
[0097] In some instances, PPEMS 6 may apply analytics for
combinations of PPE. For example, PPEMS 6 may draw correlations
between users of SRLs 11 and/or the other PPE that is used with
SRLs 11. That is, in some instances, PPEMS 6 may determine the
likelihood of a safety event based not only on usage data from SRLs
11, but also from usage data from other PPE being used with SRLs
11. In such instances, PPEMS 6 may include one or more safety
models that are constructed from data of known safety events from
one or more devices other than SRLs 11 that are in use with SRLs
11.
[0098] FIG. 4A illustrates an example of internal components of SRL
11, as contained within housing 96. In the illustrated example, SRL
11 includes an encoder 130 that is incorporated on a drum of SRL
11. In some examples, encoder 130 may comprise at least a portion
of extension sensor 106 shown in FIG. 3. Encoder 130 may be
configured to measure line length of lifeline 92 as lifeline 92 is
extended from housing 96. Encoder 130 may be configured to output
data that is indicative of a number of turns of the drum upon which
lifeline 92 is wound, an angular speed of the drum, and/or an
angular acceleration of the drum, or the like. Such data may be
used to determine a line length of lifeline 92 (e.g., a quantity of
lifeline 92 that has been extended from housing 96), a linear speed
with which lifeline 92 is extended, and/or a linear acceleration
with which lifeline 92 is extended.
[0099] For example, in some instances, encoder 130 may be
configured as a rotary encoder (also referred to as a shaft
encoder) that converts the angular position or motion of a shaft or
axle of the drum upon which lifeline 92 is wound to an analog or
digital code. Encoder 130 may be configured as an absolute encoders
that outputs data indicative of a current position of the shaft or
an incremental encoder that provides data indicative of the motion
of the shaft, which SRL 11 may further process to determine
position, distance, speed, acceleration or the like.
[0100] FIG. 4B illustrates another example of internal components
of SRL 11, as contained within housing 96 (FIG. 3). In the
illustrated example, SRL 11 includes eccentric deflectors 136A-136C
(collectively, eccentric deflectors 136). Each of eccentric
deflectors 136 includes a weighted end 138 that moves outward as
angular acceleration and/or angular speed reach a predetermined
threshold to overcome biasing force, e.g., a spring tension,
resisting outward movement. As such, eccentric deflectors 136 may
generate data that indicates a speed and/or acceleration of a drum
upon which lifeline 92 is wound as lifeline 92 is extended from
housing 96. From the speed and/or acceleration data, SRL 11 may
determine that a fall has occurred.
[0101] FIG. 5 illustrates an example of one of hubs 14 in greater
detail. For example, hub 14 includes one or more processors 130,
memory 132 that may store usage data 134, alert data 136 and/or
rules 136, communication unit 140, sensors 142, user interface 144,
and remote interface 146. It should be understood that the
architecture and arrangement of hub 14 illustrated in FIG. 5 is
shown for exemplary purposes only. In other examples, hub 14 may be
configured in a variety of other ways having additional, fewer, or
alternative components than those shown in FIG. 5. For example, hub
14 may also include one or more batteries, charging components, or
the like not shown in FIG. 5. In addition, while shown as a
wearable device in the example of FIG. 5, in other examples, hub 14
may be implemented as stand-alone device deployed in a particular
environment.
[0102] In general, hub 14 may enable and facilitate communication
between SRLs 11 and PPEMS 6. For examples, SRLs 11 as well as other
PPEs for a respective worker may communicate with hub 14 via
Bluetooth or other short range protocol, and hub 14 may communicate
with PPEMs 6 via wireless communications, such as via 802.11 WiFi
protocols, or the like. In some examples, as described in greater
detail herein, hub 14 may also implement rules that characterize
the likelihood of a safety event (e.g., from PPEMs), generate
and/or output alerts, or perform a variety of other functions.
[0103] Processors 130, in one example, are configured to implement
functionality and/or process instructions for execution within hub
14. For example, processors 130 may be capable of processing
instructions stored by memory 132. Processors 130 may include, for
example, microprocessors, digital signal processors (DSPs),
application specific integrated circuits (ASICs),
field-programmable gate array (FPGAs), or equivalent discrete or
integrated logic circuitry.
[0104] Memory 132 may include a computer-readable storage medium or
computer-readable storage device. In some examples, memory 132 may
include one or more of a short-term memory or a long-term memory.
Memory 132 may include, for example, random access memories (RAM),
dynamic random access memories (DRAM), static random access
memories (SRAM), magnetic hard discs, optical discs, flash
memories, or forms of electrically programmable memories (EPROM) or
electrically erasable and programmable memories (EEPROM).
[0105] In some examples, memory 132 may store an operating system
(not shown) or other application that controls the operation of
components of hub 14. For example, the operating system may
facilitate the communication of data from memory 132 to
communication unit 140. In some examples, memory 132 is used to
store program instructions for execution by processors 100. Memory
132 may also be configured to store information within hub 14
during operation. In the example shown in FIG. 5, memory 132 may
store usage data 134, alert data 136, and/or rules 138, as
described in greater detail below.
[0106] Hub 14 may use communication unit 140 to communicate with
external devices via one or more wired or wireless connections.
Communication unit 140 may include various mixers, filters,
amplifiers and other components designed for signal modulation, as
well as one or more antennas and/or other components designed for
transmitting and receiving data. Communication unit 140 may send
and receive data to other computing devices using any one or more
suitable data communication techniques. Examples of such
communication techniques may include TCP/IP, Ethernet, Wi-Fi,
Bluetooth, 4G, LTE, to name only a few examples. For example,
communication unit 140 may communicate with SRL 11 or other PPE via
Bluetooth or other short range protocol, and communication unit 140
may communicate with PPEMs 6 via wireless communications, such as
via 802.11 WiFi protocols, or the like.
[0107] Sensors 142 may include one or more sensors that generate
data indicative of an activity of a worker 10 associated with hub
14 and/or data indicative of an environment in which hub 14 is
located. Sensors 142 may include, as examples, one or more
accelerometers, one or more sensors to detect conditions present in
a particular environment (e.g., sensors for measuring temperature,
humidity, particulate content, noise levels, air quality, or any
variety of other characteristics of environments in which SRL 11
may be used), or a variety of other sensors.
[0108] User interface 144 may include one more user interface
devices including, as examples, a variety of lights, displays,
haptic feedback generators, speakers or the like. In general, user
interface 144 may output a status of SRL 11 and/or hub 14, as well
as any alerts for worker 10. In one example, user interface 144 may
include a plurality of multi-color LEDs that illuminate to provide
information to worker 10. In another example, user interface 144
may include a motor that is configured to vibrate hub 14 to provide
haptic feedback to worker 10.
[0109] Remote interface 146 is configured to generate data for
output at clients 62 (FIG. 2). For example, remote interface 146
may generate data indicative of a status of SRL 11 and/or hub 14.
For example, remote interface 146 may generate data that indicates
whether SRL 11 is connected to hub 14 and/or information about
components of SRL 11. That is, remote interface 146 may generate
data indicative of, as examples, remaining service life of SRL 11,
a status of a battery of SRL 11, a quantity of lifeline 92 that is
extended from housing 96 (FIG. 3), a maximum extension distance of
lifeline 92, a number of extension/retraction cycles of lifeline
92, whether maintenance or replacement of SRL 11 is needed,
position/speed/acceleration of lifeline 92, or the like. Remote
interface 146 may additionally or alternatively generate data that
is indicative of any alerts issued by hub 14.
[0110] According to aspects of this disclosure, hub 14 may store
usage data 134 from sensors of SRL 11. For example, as described
herein, sensors of SRL 11 may generate data regarding operation of
SRL 11 that is indicative of activities of worker 10 and transmit
the data in real-time or near real-time to hub 14. In some
examples, hub 134 may immediately relay usage data 134 to another
computing device, such as PPEMS 6, via communication unit 140. In
other examples, memory 132 may store usage data 134 for some time
prior to uploading the data to another device. For example, in some
instances, communication unit 140 may be able to communicate with
SRL 11 but may not have network connectivity, e.g., due to an
environment in which SRL 11 is located and/or network outages. In
such instances, hub 14 may store usage data 134 to memory 132,
which may allow the usage data to be uploaded to another device
upon a network connection becoming available.
[0111] According to aspects of this disclosure, hub 14 may store
alert data 136 for generating alerts for output by user interface
144 and/or remote interface 146. For hub 14 may receive alert data
from PPEMS 6, end-user computing devices 16, remote users using
computing devices 18, safety stations 15, or other computing
devices. In some examples, the alert data may be based on operation
of SRL 11. For example, hub 14 may receive alert data 136 that
indicates a status of SRL 11, that SRL 11 is appropriate for the
environment in which SRL 11 is located, that the environment in
which SRL 11 is located is unsafe, or the like.
[0112] In some examples, additionally or alternatively, hub 14 may
receive alert data 136 associated with a likelihood of a safety
event. For example, as noted above, PPEMS 6 may, in some examples,
apply historical data and models to usage data from SRL 11 in order
to compute assertions, such as anomalies or predicted occurrences
of imminent safety events based on environmental conditions or
behavior patterns of a worker using SRL 11. That is, PPEMS 6 may
apply analytics to identify relationships or correlations between
sensed data from SRL 11, environmental conditions of environment in
which SRL 11 is located, a geographic region in which SRL 11 is
located, and/or other factors. PPEMS 6 may determine, based on the
data acquired across populations of workers 10, which particular
activities, possibly within certain environment or geographic
region, lead to, or are predicted to lead to, unusually high
occurrences of safety events. Hub 14 may receive alert data 136
from PPEMS 6 that indicates a relatively high likelihood of a
safety event.
[0113] Hub 14 may interpret the received alert data 136 and
generate an output at user interface 144 (e.g., an audible, visual,
or tactile output) or remote interface 146 to notify worker 10 or a
remote party of the alert condition (e.g., that the likelihood of a
safety event is relatively high, that the environment is dangerous,
that SRL 11 is malfunctioning, that one or more components of SRL
11 need to be repaired or replaced, or the like). In some
instances, hub 14 may also interpret alert data 136 and issue one
or more commands to SRL 11 to modify operation or enforce rules of
SRL 11 in order to bring operation of SRL 11 into compliance with
desired/less risky behavior.
[0114] According to aspects of this disclosure, in some instances,
hub 14 may store rules 138 for generating alert data 136 and
issuing alerts. For example, hub 14 may be configured to store
rules 138 that characterize a likelihood of a safety event, and
user interface 144 and/or remote interface may generate an output
based on a comparison of operation of the SRL 11 to rules 138.
Rules 138 may be defined based on the above-described models and/or
historical data from PPEMS 6. In some examples, PPEMS 6 may provide
hub 14 with rules 138, which may comprise a subset of rules
generated by PPEMS 6 based on one or more safety models. In such
examples, hub 14 may implement rules 138 without network
connectivity to PPEMS 6.
[0115] In general, while certain techniques or functions are
described herein as being performed by certain components, e.g.,
PPEMS 6, SRLs 11, or hubs 14, it should be understood that the
techniques of this disclosure are not limited in this way. That is,
certain techniques described herein may be performed by one or more
of the components of the described systems. For example, in some
instances, SRLs 11 may have a relatively limited sensor set and/or
processing power. In such instances, one of hubs 14 and/or PPEMS 6
may responsible for most or all of the processing of usage data,
determining the likelihood of a safety event, and the like. In
other examples, SRLs 11 may have additional sensors, additional
processing power, and/or additional memory, allowing for SRLs 11 to
perform additional techniques. Determinations regarding which
components are responsible for performing techniques may be based,
for example, on processing costs, financial costs, power
consumption, or the like.
[0116] FIG. 6 illustrates an example model applied by the personal
protection equipment management system or other devices herein with
respect to worker activity in terms of measure line speed,
acceleration and line length, where the model is arranged to define
safe regions and regions unsafe. In other words, FIG. 6 is a graph
representative of a model applied by PPEMS 6, hubs 14 or SRLs 11 to
predict the likelihood of a safety event based on measurements of
acceleration 160 of a lifeline (such as lifeline 92 shown in FIG.
3) being extracted, speed 162 of a lifeline 92 being extracted, and
length 164 of a lifeline that has been extracted. The measurements
of acceleration 160, speed 162, and length 164 may be determined
based on data collected from sensors of SRLs 11. Data represented
by the graph may be estimated or collected in a training/test
environment and the graph may be used as a "map" to distinguish
safe activities of a worker from unsafe activities.
[0117] For example, safe region 166 may represent measurements of
acceleration 160, speed 162, and length 164 that are associated
with safe activities (e.g., as determined by monitoring activities
of a worker in a test environment). Untied region 168 may represent
measurements of acceleration 160, speed 162, and length 164 that
are associated with a lifeline that is not securely anchored to a
support structure, which may be considered unsafe. Over stretched
region 170 may represent measurements of acceleration 160, speed
162, and length 164 that are associated with a lifeline that is
extended beyond normal operating parameters, which may also be
considered unsafe.
[0118] According to aspects of this disclosure, PPEMS 6, hubs 14,
or SRLs 11 may issue one or more alerts by applying a model or rule
set represented by FIG. 6 to usage data received from SRLs 11. For
example, PPEMS 6, hubs 14, or SRLs 11 may issue an alert if
measurements of acceleration 160, speed 162, or length 164 are
outside of safe region 166. In some instances, different alerts may
be issued based how far measurements of acceleration 160, speed
162, or length 164 are outside of safe region 166. For example, if
measurements of acceleration 160, speed 162, or length 164 are
relatively close to safe region 166, PPEMS 6, hubs 14, or SRLs 11
may issue a warning that the activity is of concern and may result
in a safety event. In another example, if measurements of
acceleration 160, speed 162, or length 164 are relatively far from
safe region 166, PPEMS 6, hubs 14, or SRLs 11 may issue a warning
that the activity is unsafe and has a high likelihood of an
immediate safety event.
[0119] In some instances, the data of the graph shown in FIG. 6 may
be representative of historical data and models 74B shown in FIG.
2. In this example, PPEMS 6 may compare incoming streams of data to
the map shown in FIG. 6 to determine a likelihood of a safety
event. In other instances, a similar map may additionally or
alternatively be stored to SRLs 11 and/or hubs 14, and alerts may
be issued based on the locally stored data.
[0120] While the example of FIG. 6 illustrates acceleration 160,
speed 162, and length 164, other maps have more or fewer variables
than those shown may be developed. In one example, a map may be
generated based only on a length of a lifeline. In this example, an
alert may be issued to a worker when the lifeline is extended
beyond a line length specified by the map.
[0121] FIG. 7 illustrates an example of a second model applied by
the personal protection equipment management system or other
devices herein with respect to worker activity in terms of measure
force/tension on the safety line and length, where the model is
arranged to define a safe region and regions unsafe behavior
predictive of safety events, in accordance with aspects of this
disclosure. In this example, FIG. 7 is a graph representative of a
model or ruleset applied by PPEMS 6, hubs 14 or SRLs 11 to predict
the likelihood of a safety event based on measurements of force or
tension 180 on a lifeline (such as lifeline 92 shown in FIG. 3) and
length 182 of a lifeline that has been extracted. The measurements
of force or tension 180 and length 182 may be determined based on
data collected from sensors of SRLs 11. Data represented by the
graph may be estimated or collected in a training/test environment
and the graph may be used as a "map" to distinguish safe activities
of a worker from unsafe activities.
[0122] For example, safe region 184 may represent measurements of
force or tension 180 and length 182 that are associated with safe
activities (e.g., as determined by monitoring activities of a
worker in a test environment). Untied region 186 may represent
measurements of force or tension 180 and length 182 that are
associated with a lifeline that is not securely anchored to a
support structure, which may be considered unsafe. Over stretched
region 188 may represent measurements of force or tension 180 and
length 182 that are associated with a lifeline that is extended
beyond normal operating parameters, which may also be considered
unsafe.
[0123] According to aspects of this disclosure, PPEMS 6, hubs 14,
or SRLs 11 may issue one or more alerts by applying a model or rule
set represented by FIG. 6 to usage data from SRLs 11, in a manner
similar to that described above with respect to FIG. 6. In some
instances, the data of the graph shown in FIG. 7 may be
representative of historical data and models 74B shown in FIG. 2.
In other instances, a similar map may additionally or alternatively
be stored to SRLs 11 and/or hubs 14, and alerts may be issued based
on the locally stored data.
[0124] FIGS. 8A and 8B illustrate profiles of example input streams
of event data received and processed by PPEMS 6, hubs 14 or SRLs 11
and, based on application of one or more models or rules sets,
determined to represent low risk behavior (FIG. 8A) and high risk
behavior (FIG. 8B), which results in triggering of alerts or other
responses, in accordance with aspects of this disclosure. In the
examples, FIGS. 8A and 8B illustrate profiles of example event data
determined to indicate safe activity and unsafe activity,
respectively, over a period of time. For example, the example of
FIG. 8A illustrates a speed 190 with which a lifeline (such as
lifeline 92 shown in FIG. 3) is extracted relative to a kinematic
threshold 192, while the example of FIG. 8B illustrates a speed 194
with which a lifeline (such as lifeline 92 shown in FIG. 3) is
extracted relative to threshold 192.
[0125] In some instances, the profiles shown in FIGS. 8A and 8B may
be developed and stored as historical data and models 74B of PPEMS
6 shown in FIG. 2. According to aspects of this disclosure, PPEMS
6, hubs 14, or SRLs 11 may issue one or more alerts by comparing
usage data from SRLs 11 to threshold 192. For example, PPEMS 6,
hubs 14, or SRLs 11 may issue one or more alerts when speed 194
exceeds threshold 192 in the example of FIG. 8B. In some instances,
different alerts may be issued based how much the speed exceeds
threshold 192, e.g., to distinguish risky activities from activity
is unsafe and has a high likelihood of an immediate safety
event.
[0126] FIG. 9 is an example process for predicting the likelihood
of a safety event, according to aspects of this disclosure. While
the techniques shown in FIG. 9 are described with respect to PPEMS
6, it should be understood that the techniques may be performed by
a variety of computing devices.
[0127] In the illustrated example, PPEMS 6 obtains usage data from
at least one self-retracting lifeline (SRL), such as at least one
of SRLs 11 (200). As described herein, the usage data comprises
data indicative of operation of SRL 11. In some examples, PPEMS 6
may obtain the usage data by polling SRLs 11 or hubs 14 for the
usage data. In other examples, SRLs 11 or hubs 14 may send usage
data to PPEMS 6. For example, PPEMS 6 may receive the usage data
from SRLs 11 or hubs 14 in real time as the usage data is
generated. In other examples, PPEMS 6 may receive stored usage
data.
[0128] PPEMS 6 may apply the usage data to a safety model that
characterizes activity of a user of the at least one SRL 11 (202).
For example, as described herein, the safety model may be trained
based on data from known safety events and/or historical data from
SRLs 11. In this way, the safety model may be arranged to define
safe regions and regions unsafe.
[0129] PPEMS 6 may predict a likelihood of an occurrence of a
safety event associated with the at least one SRL 11 based on
application of the usage data to the safety model (204). For
example, PPEMS 6 may apply the obtained usage data to the safety
model to determine whether the usage data is consistent with safe
activity (e.g., as defined by the model) or potentially unsafe
activity.
[0130] PPEMS 6 may generate an output in response to predicting the
likelihood of the occurrence of the safety event (206). For
example, PPEMS 6 may generate alert data when the usage data is not
consistent with safe activity (as defined by the safety model).
PPEMS 6 may send the alert data to SRL 11, a safety manager, or
another third party that indicates the likelihood of the occurrence
of the safety event.
[0131] Example 1: A method comprising: obtaining usage data from at
least one self-retracting lifeline (SRL), wherein the usage data
comprises data indicative of operation of the at least one SRL;
applying, by an analytics engine implemented at a computing device,
the usage data to a safety model that characterizes activity of a
user of the at least one SRL; predicting a likelihood of an
occurrence of a safety event associated with the at least one SRL
based on application of the usage data to the safety model; and
generating an output in response to predicting the likelihood of
the occurrence of the safety event.
[0132] Example 2: The method of Example 1, wherein the safety model
is constructed from historical data of known safety events from a
plurality of SRLs having similar characteristics to the at least
one SRL.
[0133] Example 3: The method of any of Examples 1-2, further
comprising updating the safety model based on the usage data from
the at least one SRL.
[0134] Example 4: The method of any of Examples 1-3, wherein the
safety model is constructed from data of known safety events from
one or more devices other than SRLs that are in use with the at
least one SRL.
[0135] Example 5: The method of any of Examples 1-4, further
comprising selecting the safety model based on at least one of a
configuration of the at least one SRL, a user of the at least one
SRL, an environment in which the at least one SRL is located, or
one or more other devices that are in use with the at least one
SRL.
[0136] Example 6: The method of any of Examples 1-5, wherein the
usage data representative of activity of a user of the at least one
SRL during a time period, and wherein the usage data comprises data
indicative of at least one of extension and retraction of a
lifeline of the SRL, a force applied to the lifeline of the at
least one SRL, an acceleration of the at least one SRL, a location
of the at least one SRL, or an altitude of the at least one
SRL.
[0137] Example 7: The method of any of Examples 1-6, wherein the
usage data comprises environmental data associated with an
environment in which the at least one SRL is located, such that the
likelihood of the occurrence of the safety event is based on the
environment in which the SRL is located.
[0138] Example 8: The method of any of Examples 1-7, wherein
applying the usage data to the safety model that characterizes
activity of the user comprises applying the usage data to a safety
model that is constructed from training data of known safety events
associated with a plurality of SRLs.
[0139] Example 9: The method of any of Examples 1-8, wherein
predicting the likelihood of the occurrence of the safety event
comprises identifying anomalous behavior of a user of the at least
one SRL relative to known safe behavior characterized by the safety
model.
[0140] Example 10: The method of any of Examples 1-9, wherein
predicting the likelihood of the occurrence of the safety event
further comprises identifying regions within a work environment in
which the at least one SRL is deployed that are associated with an
anomalous number of safety events.
[0141] Example 11: The method of any of Examples 1-10, wherein
applying the usage data to the safety model comprises applying the
usage data to a safety model that characterizes a speed of a user
of at least one SRL, and wherein predicting the likelihood of the
occurrence of the safety event comprises determining that the speed
of the user over a time period exceeds a speed associated with safe
activity over the time period.
[0142] Example 12: The method of any of Examples 1-11, wherein
applying the usage data to the safety model comprises applying the
usage data to a safety model that characterizes a force applied to
a lifeline of the at least one SRL by a user of the at least one
SRL, and wherein predicting the likelihood of the occurrence of the
safety event comprises determining that the force applied over a
time period exceeds a force associated with safe activity over the
time period.
[0143] Example 13: The method of any of Examples 1-12, wherein
applying the usage data to the safety model comprises applying the
usage data to a safety model that characterizes an extension length
of a lifeline of the at least one SRL, and wherein predicting the
likelihood of the occurrence of the safety event comprises
determining that the extension length exceeds or is less than an
extension length associated with safe activity over the time
period.
[0144] Example 14: The method of any of Examples 1-13, wherein
generating the output comprises generating alert data that
indicates that a safety event is likely.
[0145] Example 15: The method of any of Examples 1-14, further
comprising generating a user interface based on the usage data,
wherein the user interface indicates at least one of operation of
the at least one SRL, safety events associated with the at least
one SRL, or a geographic region in which the at least one SRL is
deployed and at least one safety event has occurred or is likely to
occur.
[0146] Example 16: A non-transitory computer-readable storage
medium encoded with instructions that, when executed, cause at
least one processor of a computing device to perform any of the
method of Examples 1-15.
[0147] Example 17: An apparatus comprising means for performing any
of the method of Examples 1-15.
[0148] It is to be recognized that depending on the example,
certain acts or events of any of the techniques described herein
can be performed in a different sequence, may be added, merged, or
left out altogether (e.g., not all described acts or events are
necessary for the practice of the techniques). Moreover, in certain
examples, acts or events may be performed concurrently, e.g.,
through multi-threaded processing, interrupt processing, or
multiple processors, rather than sequentially.
[0149] In one or more examples, the functions described may be
implemented in hardware, software, firmware, or any combination
thereof. If implemented in software, the functions may be stored on
or transmitted over a computer-readable medium as one or more
instructions or code, and executed by a hardware-based processing
unit. Computer-readable media may include computer-readable storage
media, which corresponds to a tangible medium such as data storage
media, or communication media including any medium that facilitates
transfer of a computer program from one place to another, e.g.,
according to a communication protocol. In this manner,
computer-readable media generally may correspond to (1) tangible
computer-readable storage media which is non-transitory or (2) a
communication medium such as a signal or carrier wave. Data storage
media may be any available media that can be accessed by one or
more computers or one or more processors to retrieve instructions,
code and/or data structures for implementation of the techniques
described in this disclosure. A computer program product may
include a computer-readable medium.
[0150] By way of example, and not limitation, such
computer-readable storage media can comprise RAM, ROM, EEPROM,
CD-ROM or other optical disk storage, magnetic disk storage, or
other magnetic storage devices, flash memory, or any other medium
that can be used to store desired program code in the form of
instructions or data structures and that can be accessed by a
computer. Also, any connection is properly termed a
computer-readable medium. For example, if instructions are
transmitted from a website, server, or other remote source using a
coaxial cable, fiber optic cable, twisted pair, digital subscriber
line (DSL), or wireless technologies such as infrared, radio, and
microwave, then the coaxial cable, fiber optic cable, twisted pair,
DSL, or wireless technologies such as infrared, radio, and
microwave are included in the definition of medium.
[0151] It should be understood, however, that computer-readable
storage media and data storage media do not include connections,
carrier waves, signals, or other transitory media, but are instead
directed to non-transitory, tangible storage media. Disk and disc,
as used herein, includes compact disc (CD), laser disc, optical
disc, digital versatile disc (DVD), floppy disk and Blu-ray disc,
where disks usually reproduce data magnetically, while discs
reproduce data optically with lasers. Combinations of the above
should also be included within the scope of computer-readable
media.
[0152] Instructions may be executed by one or more processors, such
as one or more digital signal processors (DSPs), general purpose
microprocessors, application specific integrated circuits (ASICs),
field programmable gate arrays (FPGAs), or other equivalent
integrated or discrete logic circuitry, as well as any combination
of such components. Accordingly, the term "processor," as used
herein may refer to any of the foregoing structures or any other
structure suitable for implementation of the techniques described
herein. In addition, in some aspects, the functionality described
herein may be provided within dedicated hardware and/or software
modules. Also, the techniques could be fully implemented in one or
more circuits or logic elements.
[0153] The techniques of this disclosure may be implemented in a
wide variety of devices or apparatuses, including a wireless
communication device or wireless handset, a microprocessor, an
integrated circuit (IC) or a set of ICs (e.g., a chip set). Various
components, modules, or units are described in this disclosure to
emphasize functional aspects of devices configured to perform the
disclosed techniques, but do not necessarily require realization by
different hardware units. Rather, as described above, various units
may be combined in a hardware unit or provided by a collection of
interoperative hardware units, including one or more processors as
described above, in conjunction with suitable software and/or
firmware.
[0154] Various examples have been described. These and other
examples are within the scope of the following claims.
* * * * *