U.S. patent application number 17/564029 was filed with the patent office on 2022-04-21 for advanced consumer application for mobile assessment of functional capacity and falls risk.
The applicant listed for this patent is Electronic Caregiver, Inc.. Invention is credited to Bryan John Chasko, Anthony Dohrmann, David W. Keeley.
Application Number | 20220117515 17/564029 |
Document ID | / |
Family ID | 1000006109572 |
Filed Date | 2022-04-21 |
![](/patent/app/20220117515/US20220117515A1-20220421-D00000.png)
![](/patent/app/20220117515/US20220117515A1-20220421-D00001.png)
![](/patent/app/20220117515/US20220117515A1-20220421-D00002.png)
![](/patent/app/20220117515/US20220117515A1-20220421-D00003.png)
![](/patent/app/20220117515/US20220117515A1-20220421-D00004.png)
![](/patent/app/20220117515/US20220117515A1-20220421-D00005.png)
![](/patent/app/20220117515/US20220117515A1-20220421-D00006.png)
![](/patent/app/20220117515/US20220117515A1-20220421-D00007.png)
![](/patent/app/20220117515/US20220117515A1-20220421-D00008.png)
![](/patent/app/20220117515/US20220117515A1-20220421-D00009.png)
United States Patent
Application |
20220117515 |
Kind Code |
A1 |
Dohrmann; Anthony ; et
al. |
April 21, 2022 |
Advanced Consumer Application for Mobile Assessment of Functional
Capacity and Falls Risk
Abstract
Systems and methods for monitoring movement capabilities using
clinical mobility based assessments of a user are provided herein.
In embodiments, methods include: providing, using a mobile device
comprising an inertial measurement device, a clinical mobility
based assessment to a user; and generating, using the inertial
measurement device, inertial data of the user that is indicative of
movement capabilities of the user based on the clinical mobility
based assessment. Embodiments include logging the inertial data of
the user locally to the mobile device resulting in locally logged
inertial data of the user; processing in real-time the locally
logged inertial data of the user to determine position and
orientation of the mobile device during the clinical mobility based
assessment; and determining, using the position and the orientation
of the mobile device during the clinical mobility based assessment,
a physical movement assessment of the user associated with the
clinical mobility based assessment.
Inventors: |
Dohrmann; Anthony; (El Paso,
TX) ; Chasko; Bryan John; (Las Cruces, NM) ;
Keeley; David W.; (Frisco, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Electronic Caregiver, Inc. |
Las Cruces |
NM |
US |
|
|
Family ID: |
1000006109572 |
Appl. No.: |
17/564029 |
Filed: |
December 28, 2021 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
16289551 |
Feb 28, 2019 |
11213224 |
|
|
17564029 |
|
|
|
|
62645053 |
Mar 19, 2018 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01C 21/16 20130101;
G06F 3/14 20130101; A61B 5/6898 20130101; A61B 2562/0219 20130101;
G01P 15/08 20130101; A61B 5/0022 20130101; A61B 5/744 20130101;
G16H 50/30 20180101; G01C 19/00 20130101; A61B 5/1124 20130101;
A61B 5/4884 20130101; A61B 5/7475 20130101; G16H 40/67
20180101 |
International
Class: |
A61B 5/11 20060101
A61B005/11; G06F 3/14 20060101 G06F003/14; G01C 21/16 20060101
G01C021/16; G01C 19/00 20060101 G01C019/00; G01P 15/08 20060101
G01P015/08; A61B 5/00 20060101 A61B005/00; G16H 40/67 20060101
G16H040/67; G16H 50/30 20060101 G16H050/30 |
Claims
1. A system for monitoring movement capabilities of a user using
clinical mobility based assessments, the system comprising: a
mobile device comprising an inertial measurement device, the
inertial measurement device comprising: a gyroscope; and an
accelerometer; at least one processor; and a memory storing
processor-executable instructions, wherein the at least one
processor is configured to implement the following operations upon
executing the processor-executable instructions: providing a
clinical mobility based assessment to a user; generating, using the
inertial measurement device, inertial data of the user that is
indicative of movement capabilities of the user based on the
clinical mobility based assessment; logging the inertial data of
the user locally to the mobile device resulting in locally logged
inertial data of the user; processing in real-time the locally
logged inertial data of the user to determine position and
orientation of the mobile device during the clinical mobility based
assessment; determining, using the position and the orientation of
the mobile device during the clinical mobility based assessment, a
physical movement assessment of the user associated with the
clinical mobility based assessment; and displaying at least a
portion of the physical movement assessment to the user; and
wherein the processing in real-time the locally logged inertial
data of the user to determine position and orientation of the
mobile device during the clinical mobility based assessment
comprises: segmenting and aligning the locally logged inertial data
of the user resulting in segmented and aligned inertial data of the
user; gravitational acceleration counterbalancing of the segmented
and aligned inertial data of the user resulting in counterbalanced
inertial data of the user; determining velocity of the mobile
device during the clinical mobility based assessment using the
counterbalanced inertial data of the user; drift compensating the
velocity of the mobile device during the clinical mobility based
assessment resulting in drift compensated velocity data; and
determining the position and the orientation of the mobile device
during the clinical mobility based assessment using the drift
compensated velocity data.
2. The system as recited in claim 1, further comprising an
interactive animated conversational graphical user interface
displayed by the mobile device; wherein the at least one processor
is further configured to implement an operation of displaying a
representation of the clinical mobility based assessment via the
interactive animated conversational graphical user interface.
3. The system as recited in claim 1, wherein the clinical mobility
based assessment includes one or more of a test duration, a turning
duration, a sit-to-stand duration, a stand-to-sit duration, a
number of sit-to-stand repetitions completed within a predetermined
period of time, and a number of stand-to-sit repetitions completed
within a predetermined period of time.
4. The system as recited in claim 1, wherein the inertial data of
the user that is indicative of movement capabilities of the user
based on the clinical mobility based assessment comprises gyroscope
data generated using the gyroscope; and accelerometer data
generated using the accelerometer.
5. The system as recited in claim 1, wherein the at least one
processor is further configured to implement an operation of:
determining features of functional movements of the user based on
the position and the orientation of the mobile device during the
clinical mobility based assessment, the features of functional
movements including one or more of: time to completion of a task,
rate to completion of a task, total repetitions of a task completed
within a predetermined period of time, decay of repetitions of a
task completed within a predetermined period of time, turn rate,
anteroposterior sway, mediolateral sway, gait characteristics,
total magnitude of displacement, vertical displacement,
mediolateral displacement, and resultant displacement.
6. The system as recited in claim 1, wherein the physical movement
assessment to the user includes one or more of a static stability
of the user, dynamic stability of the user, postural stability of
the user, balance of the user, mobility of the user, fall risk of
the user, lower body muscular strength of the user, lower body
muscular endurance of the user, lower body muscular flexibility of
the user, upper body muscular strength of the user, and upper body
muscular endurance of the user.
7. The system as recited in claim 1, wherein the at least one
processor is further configured to implement operations of:
receiving the locally logged inertial data of the user and the
physical movement assessment of the user; conducting a longitude
physical movement assessment analysis using the physical movement
assessment of the user associated with the clinical mobility based
assessment; and displaying at least a portion of the longitude
physical movement assessment analysis to the user.
8. The system as recited in claim 7, wherein the conducting the
longitude physical movement assessment analysis comprises:
receiving a predetermined threshold of change in physical movement
associated with a domain from a cloud-based normative data storage;
comparing the physical movement assessment of the user with the
predetermined threshold of change in physical movement;
determining, based on the comparing, that the physical movement
assessment exceeds the predetermined threshold of change in
physical movement; and displaying, if the physical movement
assessment exceeds the predetermined threshold of change in
physical movement, a longitude mobility assessment to the user.
9. A method for monitoring movement capabilities of a user using
clinical mobility based assessments, the method comprising:
providing, using a mobile device comprising an inertial measurement
device, a clinical mobility based assessment to a user; generating,
using the inertial measurement device, inertial data of the user
that is indicative of movement capabilities of the user based on
the clinical mobility based assessment; logging the inertial data
of the user locally to the mobile device resulting in locally
logged inertial data of the user; processing in real-time the
locally logged inertial data of the user to determine position and
orientation of the mobile device during the clinical mobility based
assessment; determining, using the position and the orientation of
the mobile device during the clinical mobility based assessment, a
physical movement assessment of the user associated with the
clinical mobility based assessment; and displaying, using the
mobile device, at least a portion of the physical movement
assessment to the user; wherein the processing in real-time the
locally logged inertial data of the user to determine position and
orientation of the mobile device during the clinical mobility based
assessment comprises: segmenting and aligning the locally logged
inertial data of the user resulting in segmented and aligned
inertial data of the user; integrating angular orientation of the
segmented and aligned inertial data of the user resulting in
counterbalanced inertial data of the user; determining velocity of
the mobile device during the clinical mobility based assessment
using the counterbalanced inertial data of the user; drift
compensating the velocity of the mobile device during the clinical
mobility based assessment resulting in drift compensated velocity
data; and determining the position and the orientation of the
mobile device during the clinical mobility based assessment using
the drift compensated velocity data.
10. The method as recited in claim 9, further comprising;
displaying a representation of the clinical mobility based
assessment via an interactive animated conversational graphical
user interface displayed by the mobile device.
11. The method as recited in claim 9, wherein the clinical mobility
based assessment includes one or more of a test duration, a turning
duration, a sit-to-stand duration, a stand-to-sit duration, a
number of sit-to-stand repetitions completed within a predetermined
period of time, and a number of stand-to-sit repetitions completed
within a predetermined period of time.
12. The method as recited in claim 9, wherein the inertial data of
the user that is indicative of movement capabilities of the user
based on the clinical mobility based assessment comprises gyroscope
data generated using a gyroscope; and accelerometer data generated
using an accelerometer.
13. The method as recited in claim 9, wherein the processing in
real-time the locally logged inertial data of the user to determine
position and orientation of the mobile device during the clinical
mobility based assessment comprises: segmenting and aligning the
locally logged inertial data of the user resulting in segmented and
aligned inertial data of the user; gravitational acceleration
counterbalancing of the segmented and aligned inertial data of the
user resulting in counterbalanced inertial data of the user;
determining velocity of the mobile device during the clinical
mobility based assessment using the counterbalanced inertial data
of the user; drift compensating the velocity of the mobile device
during the clinical mobility based assessment resulting in drift
compensated velocity data; and determining the position and the
orientation of the mobile device during the clinical mobility based
assessment using the drift compensated velocity data.
14. The method as recited in claim 9, further comprising:
determining features of functional movements of the user based on
the position and the orientation of the mobile device during the
clinical mobility based assessment, the features of functional
movements including one or more of: time to completion of a task,
rate to completion of a task, total repetitions of a task completed
within a predetermined period of time, decay of repetitions of a
task completed within a predetermined period of time, turn rate,
anteroposterior sway, mediolateral sway, gait characteristics,
total magnitude of displacement, vertical displacement,
mediolateral displacement, and resultant displacement.
15. The method as recited in claim 9, wherein the physical movement
assessment to the user includes one or more of a static stability
of the user, dynamic stability of the user, postural stability of
the user, balance of the user, mobility of the user, fall risk of
the user, lower body muscular strength of the user, lower body
muscular endurance of the user, lower body muscular flexibility of
the user, upper body muscular strength of the user, and upper body
muscular endurance of the user.
16. The method as recited in claim 9, further comprising: receiving
the locally logged inertial data of the user and the physical
movement assessment of the user; conducting a longitude physical
movement assessment analysis using the physical movement assessment
of the user associated with the clinical mobility based assessment;
and displaying at least a portion of the longitude physical
movement assessment analysis to the user.
17. A non-transitory computer readable medium having embodied
thereon instructions being executable by at least one processor to
perform a method for monitoring movement capabilities of a user
using clinical mobility based assessments, the method comprising:
providing, using a mobile device comprising an inertial measurement
device, a clinical mobility based assessment to a user; generating,
using the inertial measurement device, inertial data of the user
that is indicative of movement capabilities of the user based on
the clinical mobility based assessment; logging the inertial data
of the user locally to the mobile device resulting in locally
logged inertial data of the user; processing in real-time the
locally logged inertial data of the user to determine position and
orientation of the mobile device during the clinical mobility based
assessment; determining, using the position and the orientation of
the mobile device during the clinical mobility based assessment, a
physical movement assessment of the user associated with the
clinical mobility based assessment; and displaying, using the
mobile device, at least a portion of the physical movement
assessment to the user; wherein the processing in real-time the
locally logged inertial data of the user to determine position and
orientation of the mobile device during the clinical mobility based
assessment comprises: segmenting and aligning the locally logged
inertial data of the user resulting in segmented and aligned
inertial data of the user; integrating angular orientation of the
segmented and aligned inertial data of the user resulting in
counterbalanced inertial data of the user; determining velocity of
the mobile device during the clinical mobility based assessment
using the counterbalanced inertial data of the user; drift
compensating the velocity of the mobile device during the clinical
mobility based assessment resulting in drift compensated velocity
data; and determining the position and the orientation of the
mobile device during the clinical mobility based assessment using
the drift compensated velocity data.
18. The method as recited in claim 17, further comprising;
displaying a representation of the clinical mobility based
assessment via an interactive animated conversational graphical
user interface displayed by the mobile device.
19. The method as recited in claim 17, wherein the clinical
mobility based assessment includes one or more of a test duration,
a turning duration, a sit-to-stand duration, a stand-to-sit
duration, a number of sit-to-stand repetitions completed within a
predetermined period of time, and a number of stand-to-sit
repetitions completed within a predetermined period of time.
20. The method as recited in claim 17, wherein the inertial data of
the user that is indicative of movement capabilities of the user
based on the clinical mobility based assessment comprises gyroscope
data generated using a gyroscope; and accelerometer data generated
using an accelerometer.
21. The method as recited in claim 17, wherein the clinical
mobility based assessment includes an upper extremity movement of
elbow flexion repetitions completed within a predetermined period
of time.
22. The method as recited in claim 17, wherein the clinical
mobility based assessment includes an upper extremity movement of
distance reached with the user's hand.
23. The method as recited in claim 17, wherein the clinical
mobility based assessment includes the user to stand to test
balance and stability.
24. The method as recited in claim 17, wherein the at least one
processor is further configured to implement operations of:
receiving the locally logged inertial data of the user and the
physical movement assessment of the user; conducting a longitude
physical movement assessment analysis using the physical movement
assessment of the user associated with the clinical mobility based
assessment; and displaying at least a portion of the longitude
physical movement assessment analysis to the user.
25. The method as recited in claim 24, wherein the conducting the
longitude physical movement assessment analysis comprises:
receiving a predetermined threshold of change in physical movement
associated with a domain from a cloud-based normative data storage;
comparing the physical movement assessment of the user with the
predetermined threshold of change in physical movement;
determining, based on the comparing, that the physical movement
assessment stays within a predetermined maximum and minimum
threshold of change in physical movement; and displaying, if the
physical movement assessment exceeds the predetermined threshold of
change in physical movement, a longitude mobility assessment to the
user.
Description
RELATED APPLICATION
[0001] This continuation-in-part application claims the priority
benefit of U.S. Non-Provisional patent application Ser. No.
16/289,551 filed on Feb. 28, 2019 and titled "Consumer Application
for Mobile Assessment of Functional Capacity and Falls Risk," which
claims the priority benefit of U.S. Provisional Application Ser.
No. 62/645,053, filed on Mar. 19, 2018 titled "Consumer Application
for Mobile Assessment of Functional Capacity and Falls Risk," all
of which are hereby incorporated by reference herein in their
entireties including all appendices and all references cited
therein.
FIELD OF INVENTION
[0002] The present technology relates to a connected device
software application. More specifically, but not by limitation, the
present technology relates, to an application capable of assessing
a user's real-time fall risk when installed onto a commercially
available mobile device equipped with inertial measurement
capabilities, having Internet and/or cellular connectivity, and
voice communication technology.
BACKGROUND
[0003] The approaches described in this section could be pursued,
but are not necessarily approaches that have previously been
conceived or pursued. Therefore, unless otherwise indicated, it
should not be assumed that any of the approaches described in this
section qualify as prior art merely by virtue of their inclusion in
this section.
[0004] In response to the numerous risks associated with aging, and
the fact that the population of the United States is rapidly aging,
the effort to maintain independence has led to the development of a
number of applications focused on various aspects of health
monitoring. Most of these applications have been developed in a
manner such that they include capabilities for monitoring
biological factors such as; blood pressure, heart rate, blood
glucose levels, and/or sleep. While evidence suggests these
biological signals associated with overall health and that
consistent monitoring of parameters such as these can contribute to
improved health, currently available health applications do not
provide the capability to consistently monitor a user's capacity
for producing motion. Additionally, these current health monitoring
applications are generally not self-contained and many times
require hardware in additional to that on which they have been
installed. The present technology provides a self-contained
comprehensive method of evaluating a user's movement capabilities
and provides non-invasive methods to directly monitor and identify
declines in functional capacity. The results of these critical
motion assessments can be easily accessed by the user and displayed
on the user's mobile device in various formats.
SUMMARY
[0005] In some embodiments the present disclosure is directed to a
system of one or more computers which can be configured to perform
particular operations or actions by virtue of having software,
firmware, hardware, or a combination thereof installed on the
system that in operation causes or cause the system to perform
actions and/or method steps as described herein.
[0006] According to some embodiments the present technology is
directed to a method for monitoring movement capabilities of a user
using clinical mobility based assessments, the method comprising:
(a) providing, using a mobile device comprising an inertial
measurement device, a clinical mobility based assessment to a user;
(b) generating, using the inertial measurement device, inertial
data of the user that is indicative of movement capabilities of the
user based on the clinical mobility based assessment; (c) logging
the inertial data of the user locally to the mobile device
resulting in locally logged inertial data of the user; (d)
processing in real-time the locally logged inertial data of the
user to determine position and orientation of the mobile device
during the clinical mobility based assessment; (e) determining,
using the position and the orientation of the mobile device during
the clinical mobility based assessment, a physical movement
assessment of the user associated with the clinical mobility based
assessment; and (f) displaying, using the mobile device, at least a
portion of the physical movement assessment to the user.
[0007] In various embodiments the method includes displaying a
representation of the clinical mobility based assessment via an
interactive animated conversational graphical user interface
displayed by the mobile device.
[0008] In some embodiments the method includes the clinical
mobility based assessment includes one or more of a test duration,
a turning duration, a sit-to-stand duration, a stand-to-sit
duration, a number of sit-to-stand repetitions completed within a
predetermined period of time, and a number of stand-to-sit
repetitions completed within a predetermined period of time.
[0009] In various embodiments the inertial data of the user that is
indicative of movement capabilities of the user based on the
clinical mobility based assessment comprises gyroscope data
generated using a gyroscope; and accelerometer data generated using
an accelerometer.
[0010] In some embodiments the processing in real-time the locally
logged inertial data of the user to determine position and
orientation of the mobile device during the clinical mobility based
assessment comprises: segmenting and aligning the locally logged
inertial data of the user resulting in segmented and aligned
inertial data of the user; gravitational acceleration
counterbalancing of the segmented and aligned inertial data of the
user resulting in counterbalanced inertial data of the user;
determining velocity of the mobile device during the clinical
mobility based assessment using the counterbalanced inertial data
of the user; drift compensating the velocity of the mobile device
during the clinical mobility based assessment resulting in drift
compensated velocity data; and determining the position and the
orientation of the mobile device during the clinical mobility based
assessment using the drift compensated velocity data.
[0011] In various embodiments the processing in real-time the
locally logged inertial data of the user to determine position and
orientation of the mobile device during the clinical mobility based
assessment comprises: segmenting and aligning the locally logged
inertial data of the user resulting in segmented and aligned
inertial data of the user; integrating angular orientation of the
segmented and aligned inertial data of the user resulting in
counterbalanced inertial data of the user; determining velocity of
the mobile device during the clinical mobility based assessment
using the counterbalanced inertial data of the user; drift
compensating the velocity of the mobile device during the clinical
mobility based assessment resulting in drift compensated velocity
data; and determining the position and the orientation of the
mobile device during the clinical mobility based assessment using
the drift compensated velocity data.
[0012] In some embodiments the method further comprises:
determining features of functional movements of the user based on
the position and the orientation of the mobile device during the
clinical mobility based assessment, the features of functional
movements including one or more of: time to completion of a task,
rate to completion of a task, total repetitions of a task completed
within a predetermined period of time, decay of repetitions of a
task completed within a predetermined period of time, turn rate,
anteroposterior sway, mediolateral sway, gait characteristics,
total magnitude of displacement, vertical displacement,
mediolateral displacement, and resultant displacement.
[0013] In various embodiments the method the physical movement
assessment to the user includes one or more of a static stability
of the user, dynamic stability of the user, postural stability of
the user, balance of the user, mobility of the user, fall risk of
the user, lower body muscular strength of the user, lower body
muscular endurance of the user, lower body muscular flexibility of
the user, upper body muscular strength of the user, and upper body
muscular endurance of the user.
[0014] In some embodiments the method further comprises: receiving
the locally logged inertial data of the user and the physical
movement assessment of the user; conducting a longitude physical
movement assessment analysis using the physical movement assessment
of the user associated with the clinical mobility based assessment;
and displaying at least a portion of the longitude physical
movement assessment analysis to the user.
DESCRIPTION OF THE DRAWINGS
[0015] Certain embodiments of the present technology are
illustrated by the accompanying figures. It will be understood that
the figures are not necessarily to scale. It will be understood
that the technology is not necessarily limited to the particular
embodiments illustrated herein.
[0016] FIG. 1 shows a system for monitoring movement capabilities
of a user using clinical mobility based assessments according to
embodiments of the present technology.
[0017] FIG. 2 illustrates an exemplary inertial data processing
algorithm according to embodiments of the present technology.
[0018] FIG. 3 shows a communication system between a system for
monitoring movement capabilities of a user using clinical mobility
based assessments and cloud-based platforms according to
embodiments of the present technology.
[0019] FIG. 4A shows results of an inertial data processing
algorithm for analysis of a chair stand clinical mobility based
assessment according to embodiments of the present technology.
[0020] FIG. 4B depicts results of an inertial data processing
algorithm for analysis of a timed up-and-go clinical mobility based
assessment according to embodiments of the present technology.
[0021] FIG. 5A depicts a table showing movement assessments for
determination functional movement capacity of a user according to
embodiments of the present technology.
[0022] FIG. 5B depicts a table showing features extracted from
inertial data of the user that describe functional movements
following application analysis algorithms describing user
functional movement capacity according to embodiments of the
present technology.
[0023] FIG. 6 shows depicts a process flow diagram showing a method
for monitoring movement capabilities of a user using clinical
mobility based assessments according to embodiments of the present
technology.
[0024] FIG. 7 illustrates an exemplary computer system that may be
used to implement embodiments of the present technology.
DETAILED DESCRIPTION
[0025] The detailed embodiments of the present technology are
disclosed here. It should be understood, that the disclosed
embodiments are merely exemplary of the invention, which may be
embodied in multiple forms. Those details disclosed herein are not
to be interpreted in any form as limiting, but as the basis for the
claims.
[0026] In various embodiments it is an object of the present
technology is a software application to provide monitoring and
assessment of functional motion capacity of a user through simple
interaction with an inertial measurement unit equipped mobile
device. As such, the software application functions to consistently
evaluate the motion characteristics of a user's and report how
those motion characteristics relate to the real-time functional
capacity of the user. The software application also provides a user
with the capability for assessing performance on a variety of
fundamental movement tests. Additionally, the capacity of the
software application to utilize cloud-based storage and compute
functionality provides the capability for quick storage, retrieval
and assessment of multiple tests in such a manner that real-time
declines in functional movement capacity can be identified and
reported. Additional advantages of the software application are
apparent from the detailed embodiment descriptions and accompanying
drawings, which set forth embodiments of the present
technology.
[0027] FIG. 1 shows system 100 for monitoring movement capabilities
of a user using clinical mobility based assessments according to
embodiments of the present technology. The system 100 shows a user
110 that may access a mobile device 120. The mobile device 120
comprises an inertial measurement device 130. The inertial
measurement device 130 may be a chip, and the like, installed on
the mobile device 120. The inertial measurement device 130
comprises a gyroscope 140 and an accelerometer 150. The mobile
device 120 further comprises an application 155 (e.g., a software
application). The mobile device 120 uses a communications network
160 for communication with functional test system 170,
balance/stability system 180, and gait analysis system 190.
[0028] In various embodiments the application 155 is an Electronic
Caregiver developed mobile application capable of monitoring the
movement capabilities of the user 110. When in use, the application
155 embodies the capability for the collection, processing,
storage, and analysis of data describing motion characteristics of
the user 110 during various clinical mobility based assessments.
For example, a clinical mobility based assessment may be a motion
task. In various embodiments a clinical mobility based assessment
may be a test duration, a turning duration, a sit-to-stand
duration, a stand-to-sit duration, a number of sit-to-stand
repetitions completed within a predetermined period of time, and a
number of stand-to-sit repetitions completed within a predetermined
period of time. For example, the clinical mobility based
assessments described in FIG. 5A and FIG. 5B. Exemplary clinical
mobility based assessments (e.g., motion tasks) include timed
up-and-go test, 30 second chair stand test, four stage balance
test, gait analysis, functional reach test, sit and teach test, 5
chair stand test, 10 chair stand test, arm curl test, and postural
stability using the mobile device 120 communicating with the
functional test system 170, the balance/stability system 180, and
the gait analysis system 190,
[0029] In various embodiments the user 110 may access the mobile
device 120 by accessing a display of a representation of the
clinical mobility based assessment via an interactive animated
conversational graphical user interface displayed by the mobile
device 120. Embodiments of the present technology include
providing, using the mobile device 120 comprising the inertial
measurement device 130, a clinical mobility based assessment to a
user and generating, using the inertial measurement device 130,
inertial data of the user 110 that is indicative of movement
capabilities of the user 110 based on the clinical mobility based
assessment. Embodiments comprise logging the inertial data of the
user 110 locally to the mobile device 120 resulting in locally
logged inertial data of the user 110. In various embodiments the
inertial data of the user 110 that is indicative of movement
capabilities of the user 110 based on the clinical mobility based
assessment comprises gyroscope data generated using the gyroscope
140; and accelerometer data generated using the accelerometer
150.
[0030] FIG. 2 illustrates an exemplary inertial data processing
algorithm 200 according to embodiments of the present technology.
The inertial data processing algorithm 200 may be performed by
processing logic that may comprise hardware (e.g., dedicated logic,
programmable logic, and microcode), software (such as software run
on a general-purpose computer system or a dedicated machine), or a
combination thereof. In one or more example embodiments, the
processing logic resides at the mobile device 120, the inertial
measurement device 130, the functional test system 170, the
balance/stability system 180, and the gait analysis system 190, or
the cloud-based normative data storage 330 or combinations thereof.
The inertial data processing algorithm 200 receives inertial data
from the mobile device 120 comprising the inertial measurement
device 130. The inertial measurement device 130 comprises the
gyroscope 140 and the accelerometer 150. The inertial data
processing algorithm 200 comprises signal segmentation and
alignment 210, gravitational acceleration counterbalance 220,
integration of angular orientation 230, estimate of velocity 240,
drift determination and compensation 250, estimate of orientation
260, and estimate of position 270.
[0031] In various embodiments the inertial data processing
algorithm 200 is for monitoring movement capabilities of the user
110 using clinical mobility based assessments. Embodiments of the
present technology include processing in real-time the locally
logged inertial data of the user 110 to determine position and
orientation of the mobile device 120 during the clinical mobility
based assessment. In some embodiments the processing in real-time
the locally logged inertial data of the user 110 to determine
position and orientation of the mobile device during the clinical
mobility based assessment comprises: segmenting and aligning the
locally logged inertial data of the user 110 resulting in segmented
and aligned inertial data of the user 110. For example, segmenting
and aligning the locally logged inertial data of the user 110 is
shown in FIG. 4A. Embodiments further include gravitational
acceleration counterbalancing of the segmented and aligned inertial
data of the user 110 resulting in counterbalanced inertial data of
the user 110; determining velocity of the mobile device during the
clinical mobility based assessment using the counterbalanced
inertial data of the user 110; drift compensating the velocity of
the mobile device during the clinical mobility based assessment
resulting in drift compensated velocity data; and determining the
position and the orientation of the mobile device during the
clinical mobility based assessment using the drift compensated
velocity data.
[0032] Embodiments of the present technology include processing in
real-time the locally logged inertial data of the user 110 to
determine position and orientation of the mobile device 120 during
the clinical mobility based assessment. In some embodiments the
processing in real-time the locally logged inertial data of the
user 110 to determine position and orientation of the mobile device
during the clinical mobility based assessment comprises: segmenting
and aligning the locally logged inertial data of the user 110
resulting in segmented and aligned inertial data of the user 110;
integrating angular orientation of the segmented and aligned
inertial data of the user 110 resulting in counterbalanced inertial
data of the user 110; determining velocity of the mobile device
during the clinical mobility based assessment using the
counterbalanced inertial data of the user 110; drift compensating
the velocity of the mobile device during the clinical mobility
based assessment resulting in drift compensated velocity data; and
determining the position and the orientation of the mobile device
during the clinical mobility based assessment using the drift
compensated velocity data.
[0033] FIG. 3 shows a communication system 300 between a system for
monitoring movement capabilities of a user using clinical mobility
based assessments and cloud-based platforms according to
embodiments of the present technology. The communication system 300
comprises the mobile device 120 that comprises an application 155
(e.g., Electronic Caregiver application). The communication system
300 further comprises cloud computing network 320, cloud-based
normative data storage 330, and data streaming 340. In various
embodiments, application 155 communicates with the cloud computing
network 320.
[0034] In general, the cloud computing network 320 is a cloud-based
computing environment, which is a resource that typically combines
the computational power of a large grouping of processors (such as
within web servers) and/or that combines the storage capacity of a
large grouping of computer memories or storage devices.
[0035] The cloud computing network 320 may be formed, for example,
by a network of web servers that comprise a plurality of computing
devices, such as the computer system 700, with each server (or at
least a plurality thereof) providing processor and/or storage
resources. These servers may manage workloads provided by multiple
users (e.g., cloud resource customers or other users).
[0036] FIG. 4A shows results of an inertial data processing
algorithm for analysis of a chair stand clinical mobility based
assessment 400 according to embodiments of the present technology.
For example, an inertial data processing algorithm used to process
inertial data of the user that is indicative of movement
capabilities of the user based on the clinical mobility based
assessment may be the inertial data processing algorithm 200 shown
in FIG. 2. In more detail, FIG. 4A shows segmenting and aligning
the locally logged inertial data of the user 110 resulting in
segmented and aligned inertial data of the user 110. For example,
signal segmentation 405 of a plurality of signal segmentations is
shown in FIG. 4A. More specifically, FIG. 4A shows analysis of a
chair stand clinical mobility based assessment that is described in
more detail in Example 1.
[0037] FIG. 4B depicts results of the inertial data processing
algorithm 200 for analysis of a timed up-and-go clinical mobility
based assessment 410 according to embodiments of the present
technology. In more detail, FIG. 4B shows analysis of a timed
up-and-go clinical mobility based assessment 410 as described in
more detail in Example 2.
[0038] FIG. 5A depicts a table 500 showing movement assessments for
determination of functional movement capacity of the user 110
according to embodiments of the present technology. For example, a
clinical mobility based assessment may be a motion task. In various
embodiments a clinical mobility based assessment may be a test
duration, a turning duration, a sit-to-stand duration, a
stand-to-sit duration, a number of sit-to-stand repetitions
completed within a predetermined period of time, and a number of
stand-to-sit repetitions completed within a predetermined period of
time. Exemplary clinical mobility based assessments (e.g., motion
tasks) include timed up-and-go test, 30 second chair stand test,
four stage balance test, gait analysis, functional reach test, sit
and teach test, 5 chair stand test, 10 chair stand test, arm curl
test, and postural stability. Table 500 further shows an area of
assessment of the user 110 evaluated for each clinical mobility
based assessment (e.g., motion task).
[0039] FIG. 5B depicts a table 510 showing features extracted from
inertial data of the user 110 that describe functional movements
following application analysis algorithms describing user
functional movement capacity according to embodiments of the
present technology. For example, determining features of functional
movements of the user 110 based on the position and the orientation
of the mobile device 120 during the clinical mobility based
assessment, the features of functional movements including one or
more of: time to completion of a task, rate to completion of a
task, total repetitions of a task completed within a predetermined
period of time, decay of repetitions of a task completed within a
predetermined period of time, turn rate, anteroposterior sway,
mediolateral sway, gait characteristics, total magnitude of
displacement, vertical displacement, mediolateral displacement, and
resultant displacement. Table 510 also shows features of the user
110 extracted for each clinical mobility based assessment (e.g.,
motion task).
[0040] FIG. 6 depicts a process flow diagram showing a method 600
for monitoring movement capabilities of a user using clinical
mobility based assessments according to embodiments of the present
technology. The method 600 may be performed by processing logic
that may comprise hardware (e.g., dedicated logic, programmable
logic, and microcode), software (such as software run on a
general-purpose computer system or a dedicated machine), or a
combination thereof. In one or more example embodiments, the
processing logic resides at the mobile device 120, the inertial
measurement device 130, the functional test system 170, the
balance/stability system 180, and the gait analysis system 190, or
the cloud-based normative data storage 330 or combinations
thereof.
[0041] As shown in FIG. 6, the method 600 for monitoring movement
capabilities of a user using clinical mobility based assessments
comprises providing 610, using a mobile device comprising an
inertial measurement device, a clinical mobility based assessment
to a user. The method 600 may commence at generating 620, using the
inertial measurement device, inertial data of the user that is
indicative of movement capabilities of the user based on the
clinical mobility based assessment. The method 600 may proceed with
logging 630 the inertial data of the user locally to the mobile
device resulting in locally logged inertial data of the user; and
processing 640 in real-time the locally logged inertial data of the
user to determine position and orientation of the mobile device
during the clinical mobility based assessment. The method 600 may
proceed with determining 650, using the position and the
orientation of the mobile device during the clinical mobility based
assessment, a physical movement assessment of the user associated
with the clinical mobility based assessment; and displaying 660,
using the mobile device, at least a portion of the physical
movement assessment to the user.
[0042] In various embodiments, the method 600 optionally includes
receiving 670 the locally logged inertial data of the user and the
physical movement assessment of the user; conducting 680 a
longitude physical movement assessment analysis using the physical
movement assessment of the user associated with the clinical
mobility based assessment; and displaying 690 at least a portion of
the longitude physical movement assessment analysis to the
user.
[0043] In various embodiments the conducting the longitude physical
movement assessment analysis comprises: receiving a predetermined
threshold of change in physical movement associated with a domain
from a cloud-based normative data storage; comparing the physical
movement assessment of the user with the predetermined threshold of
change in physical movement; determining, based on the comparing,
that the physical movement assessment exceeds the predetermined
threshold of change in physical movement; and displaying, if the
physical movement assessment exceeds the predetermined threshold of
change in physical movement, a longitude mobility assessment to the
user.
EXAMPLE 1
[0044] FIG. 4A shows results of the inertial data processing
algorithm 200 for analysis of a chair stand clinical mobility based
assessment 400 according to embodiments of the present technology.
For example, a functional test may be an ability of the user 110 to
complete chair stands. This particular area of testing provides
valuable insight into lower extremity muscular strength of the user
110. One specific test, the 30-second chair stand, can be remotely
assessed by the application 155. To achieve this, the user 110
assumes a seated position in a standard chair, opens the
application 155 (e.g., Electronic Caregiver application) and
selects the corresponding test (e.g., chair stand clinical mobility
based assessment) from a drop down menu. Upon test selection, the
inertial measurement device 130 of the mobile device 120 is
activated and begins collecting inertial data of the user 110.
After a 5 second countdown, the user 110 begins the chair stand
test and completes as many sit-to-stand movements followed by
stand-to-sit repetitions as possible in the allotted time. As
depicted in FIG. 4A, the vertical acceleration signal can be
utilized for assessing the number of repetitions completed during
the test, which is the standard clinical variable assessed during
the test. Assessing the number of repetitions completed is achieved
through application of signal segmentation, which separates the
signal into distinct segments based on a quantifiable spike in the
magnitude of vertical acceleration and the application of a simple
count function that determines the number of independent segments
that were derived during processing. For example, the signal
segmentation 405 of a plurality of signal segmentations is shown in
FIG. 4A.
EXAMPLE 2
[0045] FIG. 4B depicts results of the inertial data processing
algorithm 200 for analysis of a timed up-and-go clinical mobility
based assessment 410 according to embodiments of the present
technology. For example, a functional test utilized in a geriatric
care provision setting is the timed up-and-go test. The timed
up-and-go test requires the user 110 to start in a seated position
in a standard chair, rise to a standing position, and walk a
distance of 3 meters. At the 3 meter mark, the user 110 completes a
180.degree. degree turn, walks back to the starting point, and then
sits down in the chair they started in. As the timed up-and-go test
is completed, a clinician typically records the time it takes the
patient to complete the test.
[0046] In various embodiments, systems and methods of the present
technology described herein are capable of performing the same
assessment as a clinician on demand in various embodiments. As
such, the user 110 assumes a seated position in a standard chair,
opens the application 155 (e.g., Electronic Caregiver application),
and selects a clinical mobility based assessment (i.e., the timed
up-and-go clinical mobility based assessment) from the drop down
menu on the mobile device 120. Upon test selection, the inertial
measurement device 130 is activated and begins collecting inertial
data of the user 110. After a 5 second countdown, the user 110
performs the timed up-and-go test from beginning to end. After
returning to the seated position, the user selects the end test
icon to terminate collection of inertial data. As the timed
up-and-go test is completed, the signal segmentation algorithm
segments the inertial data into a standing phase 415, an outbound
phase 420 (i.e., outbound walking), a 180.degree. turn phase 425
(i.e., turning), an inbound phase 430 (i.e., inbound walking), and
a sitting phase 435. Following segmenting and aligning the locally
logged inertial data of the user, a variety of features (e.g. time
to test completion, magnitude of vertical acceleration during
standing, and magnitude of vertical acceleration during sitting)
are used to identify characteristics of functional decline of the
user 110. For example, characteristics of functional decline may
include an increase in the time to complete the timed up-and-go
test, a decline in the peak and/or overall magnitude of vertical
acceleration during the standing phase 415 or an increase in the
peak and/or overall magnitude of vertical acceleration during the
sitting phase 435.
EXAMPLE 3
[0047] Another common functional test utilized in a geriatric care
provision setting is the postural stability test. The postural
stability test requires the user 110 to maintain a static standing
position for a period of time during which postural sway
measurements are collected. As the postural stability test is
completed, a clinician typically records the observed stability of
the user 110 completing the postural stability test as well as the
various magnitudes of acceleration that are indicative of postural
sway. Again, systems and methods of the present technology
including the application 155 (e.g., Electronic Caregiver
application) are capable of performing the same assessment as the
clinician on demand. As such, the user 110 assumes a standing
position, opens the application 155 (e.g., Electronic Caregiver
application) and selects the postural stability test from a drop
down menu. Upon selection of the postural stability test, the
inertial measurement device 130 in the mobile device 120 is
activated and begins collecting inertial data of the user 110.
After a 5 second countdown, the user 110 performs the postural
stability test for a temporal period specified by the application
155. As the postural stability test is completed, the inertial data
of the user 110 is processed and transposed into anteroposterior,
mediolateral and resultant magnitudes (i.e., accelerometer data)
and angular motion magnitudes about the anteroposterior,
mediolateral and transverse axes (i.e., gyroscopic data). The
accelerometer data and the gyroscopic data are analyzed to quantify
the magnitude of sway along and about each bodily axis which can be
used as an indicator of overall static stability and potential risk
of falling of the user 110.
[0048] Further exemplary systems and methods include a clinical
mobility based assessment with an upper extremity movement of elbow
flexion repetitions completed within a predetermined period of time
and/or an upper extremity movement of distance reached with the
user's hand. Additionally, the clinical mobility based assessment
may include the user to stand to test balance and stability.
[0049] At least one processor, according to exemplary systems and
methods, may be configured to implement operations of receiving the
locally logged inertial data of the user and the physical movement
assessment of the user, conducting a longitude physical movement
assessment analysis using the physical movement assessment of the
user associated with the clinical mobility based assessment, and
displaying at least a portion of the longitude physical movement
assessment analysis to the user. The conducting the longitude
physical movement assessment analysis may include receiving a
predetermined threshold of change in physical movement associated
with a domain from a cloud-based normative data storage, comparing
the physical movement assessment of the user with the predetermined
threshold of change in physical movement, determining, based on the
comparing, that the physical movement assessment stays within a
predetermined maximum and minimum threshold of change in physical
movement and displaying, if the physical movement assessment
exceeds the predetermined threshold of change in physical movement,
a longitude mobility assessment to the user.
[0050] FIG. 7 illustrates an exemplary computer system that may be
used to implement embodiments of the present technology. FIG. 7
shows a diagrammatic representation of a computing device for a
machine in the example electronic form of a computer system 700,
within which a set of instructions for causing the machine to
perform any one or more of the methodologies discussed herein can
be executed. In example embodiments, the machine operates as a
standalone device, or can be connected (e.g., networked) to other
machines. In a networked deployment, the machine can operate in the
capacity of a server, a client machine in a server-client network
environment, or as a peer machine in a peer-to-peer (or
distributed) network environment. The machine can be a personal
computer (PC), tablet PC, game console, set-top box (STB), personal
digital assistant (PDA), television device, cellular telephone,
portable music player (e.g., a portable hard drive audio device),
web appliance, or any machine capable of executing a set of
instructions (sequential or otherwise) that specify actions to be
taken by that machine. Further, while only a single machine is
illustrated, the term "machine" shall also be taken to include any
collection of machines that separately or jointly execute a set (or
multiple sets) of instructions to perform any one or more of the
methodologies discussed herein. Computer system 700 can be an
instance of the mobile device 120, the inertial measurement device
130, the functional test system 170, the balance/stability system
180, and the gait analysis system 190, or the cloud-based normative
data storage 330.
[0051] The example computer system 700 includes a processor or
multiple processors 705 (e.g., a central processing unit (CPU), a
graphics processing unit (GPU), or both), and a main memory 710 and
a static memory 715, which communicate with each other via a bus
720. The computer system 700 can further include a video display
unit 725 (e.g., a liquid-crystal display (LCD), organic light
emitting diode (OLED) display, or a cathode ray tube (CRT)). The
computer system 700 also includes at least one input device 730,
such as an alphanumeric input device (e.g., a keyboard), a cursor
control device (e.g., a mouse), a microphone, a digital camera, a
video camera, and so forth. The computer system 700 also includes a
disk drive unit 735, a signal generation device 740 (e.g., a
speaker), and a network interface device 745.
[0052] The disk drive unit 735 (also referred to as the disk drive
unit 735) includes a machine-readable medium 750 (also referred to
as a computer-readable medium 750), which stores one or more sets
of instructions and data structures (e.g., instructions 755)
embodying or utilized by any one or more of the methodologies or
functions described herein. The instructions 755 can also reside,
completely or at least partially, within the main memory 710,
static memory 715 and/or within the processor(s) 705 during
execution thereof by the computer system 700. The main memory 710,
static memory 715, and the processor(s) 705 also constitute
machine-readable media.
[0053] The instructions 755 can further be transmitted or received
over a communications network 760 via the network interface device
745 utilizing any one of a number of well-known transfer protocols
(e.g., Hyper Text Transfer Protocol (HTTP), CAN, Serial, and
Modbus). The communications network 760 includes the Internet,
local intranet, Personal Area Network (PAN), Local Area Network
(LAN), Wide Area Network (WAN), Metropolitan Area Network (MAN),
virtual private network (VPN), storage area network (SAN), frame
relay connection, Advanced Intelligent Network (AIN) connection,
synchronous optical network (SONET) connection, digital T1, T3, E1
or E3 line, Digital Data Service (DDS) connection, Digital
Subscriber Line (DSL) connection, Ethernet connection, Integrated
Services Digital Network (ISDN) line, cable modem, Asynchronous
Transfer Mode (ATM) connection, or an Fiber Distributed Data
Interface (FDDI) or Copper Distributed Data Interface (CDDI)
connection. Furthermore, communications network 760 can also
include links to any of a variety of wireless networks including
Wireless Application Protocol (WAP), General Packet Radio Service
(GPRS), Global System for Mobile Communication (GSM), Code Division
Multiple Access (CDMA) or Time Division Multiple Access (TDMA),
cellular phone networks, Global Positioning System (GPS), cellular
digital packet data (CDPD), Research in Motion, Limited (RIM)
duplex paging network, Bluetooth radio, or an IEEE 802.11-based
radio frequency network.
[0054] While the machine-readable medium 750 is shown in an example
embodiment to be a single medium, the term "computer-readable
medium" should be taken to include a single medium or multiple
media (e.g., a centralized or distributed database, and/or
associated caches and servers) that store the one or more sets of
instructions. The term "computer-readable medium" shall also be
taken to include any medium that is capable of storing, encoding,
or carrying a set of instructions for execution by the machine and
that causes the machine to perform any one or more of the
methodologies of the present application, or that is capable of
storing, encoding, or carrying data structures utilized by or
associated with such a set of instructions. The term
"computer-readable medium" shall accordingly be taken to include,
but not be limited to, solid-state memories, optical and magnetic
media. Such media can also include, without limitation, hard disks,
floppy disks, flash memory cards, digital video disks, random
access memory (RAM), read only memory (ROM), and the like.
[0055] The example embodiments described herein can be implemented
in an operating environment comprising computer-executable
instructions (e.g., software) installed on a computer, in hardware,
or in a combination of software and hardware. The
computer-executable instructions can be written in a computer
programming language or can be embodied in firmware logic. If
written in a programming language conforming to a recognized
standard, such instructions can be executed on a variety of
hardware platforms and for interfaces to a variety of operating
systems. Although not limited thereto, computer software programs
for implementing the present method can be written in any number of
suitable programming languages such as, for example, Hypertext
Markup Language (HTML), Dynamic HTML, XML, Extensible Stylesheet
Language (XSL), Document Style Semantics and Specification Language
(DSSSL), Cascading Style Sheets (CSS), Synchronized Multimedia
Integration Language (SMIL), Wireless Markup Language (WML),
Java.TM., Jini.TM., C, C++, C#, .NET, Adobe Flash, Perl, UNIX
Shell, Visual Basic or Visual Basic Script, Virtual Reality Markup
Language (VRML), ColdFusion.TM. or other compilers, assemblers,
interpreters, or other computer languages or platforms.
[0056] Thus, technology for monitoring movement capabilities of a
user using clinical mobility based assessments is disclosed.
Although embodiments have been described with reference to specific
example embodiments, it will be evident that various modifications
and changes can be made to these example embodiments without
departing from the broader spirit and scope of the present
application. Accordingly, the specification and drawings are to be
regarded in an illustrative rather than a restrictive sense.
* * * * *