U.S. patent application number 14/365653 was filed with the patent office on 2016-06-30 for system and method for device action and configuration based on user context detection from sensors in peripheral devices.
The applicant listed for this patent is INTEL cORPORATION. Invention is credited to Fuad AL-AMIN, Alexander ESSAIAN, Donnie H. Kim, Lakshman KRISHNAMURTHY, Jun LI, Haibin LIU, Lama NACHMAN, Indira NEGI, Sai Hemachandra VEMPRALA, Brian K. VOGEL, Xiaochao YANG.
Application Number | 20160192039 14/365653 |
Document ID | / |
Family ID | 53479455 |
Filed Date | 2016-06-30 |
United States Patent
Application |
20160192039 |
Kind Code |
A1 |
NEGI; Indira ; et
al. |
June 30, 2016 |
SYSTEM AND METHOD FOR DEVICE ACTION AND CONFIGURATION BASED ON USER
CONTEXT DETECTION FROM SENSORS IN PERIPHERAL DEVICES
Abstract
A system and method for device action and configuration based on
user context detection from sensors in peripheral devices are
disclosed. A particular embodiment includes: a peripheral device
including one or more sensors to produce sensor data; and logic, at
least a portion of which is partially implemented in hardware, the
logic configured to determine a context from the sensor data and to
perform at least one action based on the determined context, the at
least one action including modifying a configuration in a mobile
device for sending notifications to a user.
Inventors: |
NEGI; Indira; (San Jose,
CA) ; KRISHNAMURTHY; Lakshman; (Portland, OR)
; AL-AMIN; Fuad; (Sunnyvale, CA) ; YANG;
Xiaochao; (San Jose, CA) ; VOGEL; Brian K.;
(Santa Clara, CA) ; LI; Jun; (Pleasanton, CA)
; ESSAIAN; Alexander; (San Jose, CA) ; VEMPRALA;
Sai Hemachandra; (Tempe, AZ) ; Kim; Donnie H.;
(Sunnyvale, CA) ; NACHMAN; Lama; (Santa Clara,
CA) ; LIU; Haibin; (San Lorenzo, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTEL cORPORATION |
Santa Clara |
CA |
US |
|
|
Family ID: |
53479455 |
Appl. No.: |
14/365653 |
Filed: |
December 28, 2013 |
PCT Filed: |
December 28, 2013 |
PCT NO: |
PCT/US2013/078144 |
371 Date: |
January 29, 2016 |
Current U.S.
Class: |
340/870.07 |
Current CPC
Class: |
A61B 5/024 20130101;
H04W 88/02 20130101; A61B 5/0533 20130101; H04M 2250/12 20130101;
H04R 1/1091 20130101; G10L 19/00 20130101; H04M 1/7253 20130101;
A61B 5/01 20130101; H04Q 9/00 20130101; H04R 2201/107 20130101;
H04Q 2209/40 20130101; H04M 1/72569 20130101; H04M 1/6058 20130101;
H04W 68/00 20130101; A61B 5/0022 20130101; H04M 1/6066 20130101;
H04R 2499/11 20130101 |
International
Class: |
H04Q 9/00 20060101
H04Q009/00; H04W 68/00 20060101 H04W068/00; H04R 1/10 20060101
H04R001/10; A61B 5/00 20060101 A61B005/00; A61B 5/024 20060101
A61B005/024; A61B 5/01 20060101 A61B005/01; A61B 5/053 20060101
A61B005/053; H04M 1/725 20060101 H04M001/725; G10L 19/00 20060101
G10L019/00 |
Claims
1-20. (canceled)
21. A mobile device comprising: logic, at least a portion of which
is partially implemented in hardware, the logic configured to
determine a context from sensor data and to perform at least one
action based on the determined context, the at least one action
including modifying a configuration in a mobile device for sending
notifications to a user.
22. The mobile device as claimed in claim 21 wherein the sensor
data being encoded with audio signals and received on a microphone
line via a microphone conductor of an audio jack.
23. The mobile device as claimed in claim 21 including a sensor
data receiver to receive sensor data produced by one or more
sensors in a peripheral device and to provide the received sensor
data to the logic for processing.
24. The mobile device as claimed in claim 23 wherein the sensor
data receiver includes a wireless transceiver, the sensor data
being received via a wireless data transmission.
25. The mobile device as claimed in claim 21 wherein the sensor
data is of a type from the group consisting of: biometric data,
heart rate data, temperature data, pressure data, acceleration
data, galvanic skin response data, and global positioning system
data.
26. The mobile device as claimed in claim 21 wherein the mobile
device is a mobile phone.
27. A system comprising: a peripheral device including one or more
sensors to produce sensor data; and logic, at least a portion of
which is partially implemented in hardware, the logic configured to
determine a context from the sensor data and to perform at least
one action based on the determined context, the at least one action
including modifying a configuration in a mobile device for sending
notifications to a user.
28. The system as claimed in claim 27 wherein the sensor data being
encoded with audio signals and received on a microphone line via a
microphone conductor of an audio jack.
29. The system as claimed in claim 28 wherein the peripheral device
including a microcontroller coupled to the one or more sensors to
receive the sensor data generated by the one or more sensors, the
microcontroller being further configured to encode the sensor data
into an audio band signal, the peripheral device including an adder
to combine the encoded data with audio signals on the microphone
line, the adder being further configured to transfer the combined
audio signals via the microphone conductor of the audio jack.
30. The system as claimed in claim 27 including a sensor data
receiver to receive the sensor data produced by the one or more
sensors in the peripheral device and to provide the received sensor
data to the logic for processing.
31. The system as claimed in claim 30 wherein the peripheral device
includes a wireless transceiver, the sensor data being sent via a
wireless data transmission.
32. The system as claimed in claim 27 wherein the sensor data
produced by the one or more sensors in the peripheral device is
biometric data.
33. The system as claimed in claim 27 wherein the sensor data is of
a type from the group consisting of: heart rate data, temperature
data, pressure data, acceleration data, galvanic skin response
data, and global positioning system data.
34. The system as claimed in claim 27 wherein the logic is
implemented in a mobile phone.
35. The system as claimed in claim 27 wherein the peripheral device
is from the group consisting of: a headset and an earbud
accessory.
36. A non-transitory machine-useable storage medium embodying
instructions which, when executed by a machine, cause the machine
to: receive sensor data produced by one or more sensors in a
peripheral device; transfer the sensor data to a mobile device for
processing; determine a context from the sensor data; and perform
at least one action based on the determined context, the at least
one action including modifying a configuration in the mobile device
for sending notifications to a user.
37. The machine-useable storage medium as claimed in claim 36
wherein the instructions being further configured to receive the
sensor data on a microphone line via a microphone conductor of an
audio jack.
38. The machine-useable storage medium as claimed in claim 36
wherein the instructions being further configured to receive the
sensor data via a wireless data transmission.
39. The machine-useable storage medium as claimed in claim 36
wherein the sensor data produced by the one or more sensors in the
peripheral device is biometric data.
40. The machine-useable storage medium as claimed in claim 36
wherein the sensor data is of a type from the group consisting of:
heart rate data, temperature data, pressure data, acceleration
data, galvanic skin response data, and global positioning system
data.
Description
TECHNICAL FIELD
[0001] This patent application relates to electronic systems,
peripheral devices, mobile devices, and computer-implemented
software, according to various example embodiments, and more
specifically to a system and method for device action and
configuration based on user context detection from sensors in
peripheral devices.
BACKGROUND
[0002] Smartphones are becoming the predominant link between people
and information. Most current smartphones or other mobile devices
provide a capability to use mobile software applications (apps). A
mobile software application (app) can embody a defined set of
functionality and can be installed and executed on a mobile device,
such as a smartphone, as tablet device, laptop computer, a digital
camera, or other form of mobile computing, imaging, or
communications device. Conventional mobile apps are available that
focus on particular applications or functionality sets.
Additionally, most standard mobile phones and other mobile devices
have an audio/microphone connector or audio jack into which a
headset, earbuds, or other peripheral device connector can be
plugged. Most standard headsets or earbud accessories also include
a microphone so the user can both hear audio from the phone and
speak into the phone via the headset or earbud accessory. A plug
connected to the headsets, earbuds, or other peripheral device can
include separate conductive elements to transfer electrical signals
corresponding to the left ear audio, right ear audio, microphone
audio, and ground. The plug is compatible with the mobile device
audio jack. The standard headsets or earbud accessories are
configured to be placed over or attached to the ear(s) of a person,
and include one or more speakers and a microphone. The headset may
also include an arm that is attached to a housing that supports the
microphone. The arm may be movable between a stored position and an
extended, operative position. The headset, earbuds, the arm, and/or
other types of peripheral devices may include one or more
physiological or biometric sensors, environmental sensors, and/or
other types of data-producing elements.
[0003] Computing devices, communication devices, imaging devices,
electronic devices, accessories, or other types of peripheral
devices designed to be worn or attached to a user (denoted as
wearables or wearable devices) and the associated user experience
are also becoming very popular. Mobile phone headsets and earbud
accessories are examples of such wearables. Because wearable
devices are typically worn by or attached to the user all or most
of the time, it is important that wearables serve as a helpful tool
aiding the user when needed, and not become an annoying distraction
when the user is trying to focus on other things.
[0004] One form of as wearable device is a heart rate (FIR)
monitor. Existing heart rate monitoring solutions in the market are
mostly electrocardiogram (ECG) based chest straps that transmit
data to a watch that has a display. An electrocardiogram (EKG or
ECG) is a test that determines heart rate based on the electrical
activity of the heart. Other types of conventional HR monitors are
also ECG based, but only have a watch on one hand and the user
needs to pause to measure HR by touching it with the other hand. A
Valencell.TM. brand product has a PPG (photoplethysmography) based
solution for HR monitoring in earphones. PPG is an optical sensing
technique that allows measurement of blood pulsation from the skin
surface. The Valencell.TM. brand product has a sensor in the earbud
and as digital signal processor (DSP) and Bluetooth.TM. radio in a
medallion or other separate component connected to the earbuds. The
user can clip the separate medallion on their clothes or wear the
separate component. HR data is wirelessly transmitted periodically
from the medallion or other separate component to an app in a
mobile phone. Other biometric data like calories, VO2 (oxygen
consumption), etc. can also be calculated by the app in the mobile
phone. However, for wearable devices and other peripheral devices,
it is very important to be able to ascertain the user's environment
and context. Although existing systems gather some forms of
biometric data, this data is not used to determine a user's
environment and context nor used to make decisions based on a
user's dynamically determined context.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] The various embodiments are illustrated by way of example,
and not by way of limitation, in the figures of the accompanying
drawings in which:
[0006] FIG. 1 illustrates an example embodiment configured for
sending data from a peripheral device to a mobile device via the
audio/microphone wire and the audio jack;
[0007] FIG. 2 illustrates an example embodiment configured for
sending data from a peripheral device to a mobile device via a
wireless data connection;
[0008] FIG. 3 illustrates a system diagram of an example
embodiment;
[0009] FIGS. 4 through 6 illustrate examples of the placement of
sensors in various types of peripheral devices (e.g., headsets and
earbuds);
[0010] FIGS. 7 through 9 illustrate example embodiments in which
accelerometer data with a microphone input can be used to detect
movement and/or sounds of the user associated with chewing and a
type of food being eaten;
[0011] FIG. 10 is a processing flow chart illustrating an example
embodiment of a method as described herein; and
[0012] FIG. 11 shows a diagrammatic representation of a machine in
the example form of a mobile computing and/or communication system
within which a set of instructions when executed and/or processing
logic when activated may cause the machine to perform any one or
more of the methodologies described and/or claimed herein.
DETAILED DESCRIPTION
[0013] In the following description, for purposes of explanation,
numerous specific details are set forth in order to provide a
thorough understanding of the various embodiments. It will be
evident, however, to one of ordinary skill in the art that the
various embodiments may be practiced without these specific
details.
[0014] In the various embodiments described herein, a system and
method for device action and configuration based on user context
detection from sensors in peripheral devices are disclosed. The
various embodiments described herein provide various ways to
determine status and detect events to ascertain the user's context,
and to make actionable decisions based on the determined
context.
[0015] In an example embodiment described herein, a peripheral
device, such as a wearable device (e.g., a headset or earbuds), is
configured to include a data-producing component. In one
embodiment, the data-producing component can be a biometric sensor,
such as a heart rate sensor, which can produce sensor data in the
peripheral device. In the example embodiment, this sensor data can
be transmitted to a mobile device, such as a mobile phone, with
which the peripheral device is in data communications via a wired
or a wireless data connection. In an embodiment using a wireless
data connection, a standard wireless protocol, such as a Bluetooth
link, or frequency modulation (FM) radio can be used. In an
embodiment using a wired data connection, the peripheral device can
be coupled to a mobile device via an audio/microphone wire and an
audio jack of the mobile device. The sensor data can be transferred
from the peripheral device to the mobile device via the microphone
conductor of the audio jack. In various embodiments, the described
data-producing component(s) in the peripheral device can be an
accelerometer a galvanic skin response (GSR) detector, a
temperature sensor, a pressure sensor, and/or the like. It will be
apparent to those of ordinary skill in the art in view of the
disclosure herein that many other types of data-producing
components in the peripheral device may be similarly deployed. For
example, these other types of data-producing components can include
environmental sensors, motion sensors, image or video-producing
devices, audio capture devices, global positioning systems (GPS),
and the like. Additionally, these data-producing components in the
peripheral device can be grouped into sensor modules that include a
variety of different types of sensors or other types of
data-producing components. In each case, the data captured or
generated by the data-producing components in the peripheral device
can be transferred to a mobile device via a wired or wireless data
connection as described. Various embodiments are described in more
detail below.
[0016] In an example embodiment described herein, the data captured
generated by the data-producing components in the peripheral device
(denoted sensor data) can be transferred to a software application
(app) executing in the mobile device. The app can use the sensor
data to detect status and events based on the dynamic conditions
measured or determined by the sensors in the peripheral devices
that are used regularly by people. The sensor data allows the app
in a mobile device to determine the user's context (e.g., if the
user is engaged in activities and does not want to be disturbed, if
the user is looking for help and suggestions the device, or the
like). In other words, the sensor data received from the
data-producing, components in the peripheral device allow the
system to determine the context of the user. From the user context,
the system can also offer the help that the user is looking for
more easily. Based on the dynamically determined context, the
system can also automatically perform actions, suppress actions, or
configure system functionality in a manner consistent with the
dynamically determined context. These data-producing components in
the peripheral device also allow the user and the system to monitor
user wellness in real-time and over extended periods of time
thereby enabling the user to make positive lifestyle changes.
[0017] The various embodiments described herein enable the system
to receive sensor data from a plurality of peripheral device
sensors, determine user context from the sensor data, and to make
contextually-appropriate decisions for the user. In this manner,
the system can be is useful tool for the user. The system can
automatically determine user context based on real-time user and
environmental context events and status that are detected using
data from sensors installed in peripheral devices. The context
events that can be dynamically determined, by the system can
include: what the user is doing, how the user is feeling, what kind
of assistance the user needs, whether the user wants assistance or
wants not be disturbed at all, how is the user's health impacted by
certain activites, and a variety of other user-relevant states
and/or events.
[0018] Referring now to FIG. 1 an example embodiment 100 described
herein is configured for sending data from a peripheral device to a
mobile device via the audio/microphone wire and the audio jack. In
the embodiment of FIG. 1, a peripheral device 110 (e.g., headsets,
earbuds, or the like) can include one or more sensors 112. As
described above, these sensors can be biometric sensors,
environmental sensors, or other data-producing components. In a
particular example embodiment, the sensors can he optical sensors
for detecting heart rate, an infrared (IR) LED, an accelerometer,
and/or the like. The peripheral device 110 can also include a
microphone 114, which can transfer audio signals from the
peripheral device 110 to a mobile device 130 via an electrical
(audio/microphone) wire and audio jack in a standard manner. The
peripheral device 110 can also be configured to include a
microcontroller (e.g., an MSP430, or other type of
microcontroller). It will be apparent to those of ordinary skill in
the art in view of the disclosure herein that a variety of standard
microcontrollers, application specific integrated circuits (ASICs),
field programmable gate arrays (FPGAs), discrete logic circuits, or
other circuitry or logic can be similarly used as the
microcontroller of the example embodiments. The microcontroller 116
can receive the sensor data produced by the sensors 112. The sensor
data produced by the one or more sensors 112 in the peripheral
device 110 can be encoded into a modulation format and sent to the
microcontroller 116 for processing. In one example embodiment, the
sensor data is provided as I2C signals. I2C (also denoted PC or
Inter-Integrated Circuit) is a multimaster, serial, single-ended
computer bus used for attaching low-speed peripherals to a
motherboard, embedded system, cellphone, or other electronic
device. It will be apparent to those of ordinary skill in the art
that the sensor data can be provided in a variety of different
forms, formats, protocols, or signals. The microcontroller 116 can
convert the sensor data to an audio band signal using FSK
(frequency-shift keying) or other well-known encoding technique.
The converted data from the sensors 112 is added into or otherwise
combined with the audio/microphone wire signals using an adder 118
for transfer to a mobile device 130 via the standard audio jack
140.
[0019] Referring still to FIG. 1, a mobile device 130 of an example
embodiment is shown coupled to the peripheral device 110 via audio
jack 140. It will be apparent to those of ordinary skill in the art
that devices other than a mobile phone can be similarly used. For
example, the mobile device 130 can also include a smartphone, a
tablet device, laptop computer, a personal digital assistant (PDA),
global positioning system (GPS) device, an imaging device, an audio
or video player or capture device, or other form of mobile
computing, communications, or imaging device. Such mobile devices
130 can include standard components, such as an audio
encoder/decoder (codec) 132 and analog-to-digital converter (ADC)
124 as part of a sensor data receiver 133. As described above,
mobile device 110 can also include an application (app) 131, which
can comprise downloaded software, firmware, or other form of
customized processing logic. App 131 can be configured to include a
filtering component 142 and Context Detection and Device Action
Logic 332. Filtering component 142 can include a low pass filter
(LPF) 144 and a high pass filter (HPF) 146. App 131 can also be
configured as processing logic or logic, at least a portion of
which is partially implemented in hardware, the logic including the
filtering component 142 and the Context Detection and Device Action
Logic 332. The Context Detection and Device Action Logic 332 of an
example embodiment is described in more detail below.
[0020] Sensor data sent from the peripheral device 110 to the
mobile device 130 via the audio/microphone wire and the audio jack
140 is received at the sensor data receiver 133 and sampled in the
standard codec 132 provided in a conventional mobile device 130.
The codec 132 can use the analog-to-digital converter (ADC) 134, to
produce digital signals that are received by the filtering
component 142 of the app 131 executing on the mobile device 130.
The LPF 144 can be used to isolate the standard audio signals
produced by microphone 114. These audio signals can be passed to an
audio modem. The HPF 146 can be used to isolate the encoded sensor
data received from the sensors 112. The isolated sensor data can be
passed to a decoder component, which processes and analyzes the
sensor data produced in peripheral device 110. In this manner, the
example embodiment can send sensor data produced in as peripheral
device to a mobile device for processing by a mobile device app via
the audio/microphone wire and the audio jack of the mobile device.
The described embodiment provides the advantage that sensor data
can be transferred from the peripheral device to the mobile device
via the audio jack without having to modify the hardware of the
mobile device. Further, the embodiment does not require a wireless
connection to the mobile device.
[0021] However, referring now to FIG. 2, in another example
embodiment 10 data transfer from the peripheral device 150 to the
mobile device 170 can be effected using standard Bluetooth.TM. Low
Energy technology or frequency modulation (FM) radio signals
provided by a wireless transceiver 158 in the peripheral device
150. In the example embodiment shown in FIG. 2, the peripheral
device 150 (e.g., headsets, earbuds, or the like) can include one
or more sensors 112. As described above, these sensors can be
biometric sensors, environmental sensors, or other data-producing
component. Peripheral device 150 can also be configured to include
a microcontroller 156. It will be apparent to those of ordinary
skill in the art in view of the disclosure herein that a variety of
standard microcontrollers, application specific integrated circuits
(ASICs), field programmable gate arrays (FPGAs), discrete logic
circuits, or other circuitry or logic can be similarly used as the
microcontroller of the example embodiments. The microcontroller 156
can receive the sensor data produced by the sensors 112. The sensor
data produced by the one or more sensors 112 in the peripheral
device 150 can be encoded into a pre-defined data format by the
microcontroller 156 and sent to the wireless transceiver 158. The
wireless transceiver 158 allows the peripheral device 150 to
wirelessly transmit peripheral device data, such as sensor data
from sensors 112 to the mobile device 170. A wireless transceiver
172 of the sensor data receiver 173 in the mobile device 170 allows
the mobile device 170 to receive sensor data wirelessly from the
peripheral device 150. As described above, mobile device 170 can
also include an application (app) 171, which can comprise
downloaded software, firmware, or other form of customized
processing logic. The app 171 can include Context Detection and
Device Action Logic 332. The Context Detection and Device Action
Logic 332 of an example embodiment is described in more detail
below. The app 171 can receive the sensor data from the wireless
transceiver 172 via the sensor data receiver 173. In this manner,
the example embodiment can transfer sensor data produced in a
peripheral device to a mobile device for processing by a mobile
device app via a wireless data connection. The Bluetooth.TM.
solution would be simpler, but would also be more costly and would
consume more electrical power. The FM solution would require
modifications to the mobile device and may not work with any mobile
phone.
[0022] The various embodiments described herein detect a particular
state or event based on sensor data received from a peripheral
device, and then determine the broader user context based on the
state/event detection. For example, sensor data received from a
peripheral device can be used to infer the user context, which can
be used to determine if the user is having a meal, or snacking, or
drinking, or engaged in other identifiable activities, so the
system can take actions based on the broader context. According to
various example embodiments, the following usages describe examples
of the system behaviors and capabilities in response to detection
of certain user context events or states.
[0023] Referring now to FIG. 3, a system diagram 300 of an example
embodiment is illustrated. As described above, a peripheral device
310 can include a plurality of sensors or other data-generating
components 112. Examples of the placement of sensors in various
types of peripheral devices (e.g. headsets and earbuds) are shown
in FIGS. 4 through 6. Sensor data generated by these sensors 112
can be transferred to the mobile device 330 and received by the
sensor data receiver 333 in several ways as also described above.
The sensor data is processed in the mobile device 330 by Context
Detection and Device Action Logic 332 executing in a processing
environment provided by app 331. Logic 332 comprises a plurality of
processing modules for processing the sensor data to determine a
context and to perform actions based on the determined context. In
particular, the Logic 332 includes Context Determination Logic 350,
Decision Logic 360, an Event Recorder 370, and an Action Dispatcher
380. The data processing performed by each of these processing
modules is described below in relation to several described example
embodiments.
[0024] In a first example embodiment, an accelerometer and/or a
microphone or other audio capture device of data-generating
components 112 in the peripheral device 310 is used for detecting
that a user is chewing. As shown in FIGS. 7 through 9,
accelerometer data with a microphone input can be used to detect
movement and/or sounds of the user associated with chewing and a
type of food being eaten (e.g., crunchy, chewy, or soft food). This
data can be used by the Context Determination Logic 350 to
determine if the user is having a meal. In this example embodiment,
the determined context is one associated with the user having a
meal. This context determination is passed from the Context
Determination Logic 350 to the Decision Logic 360. This context
determination and any associated detected events or states can also
be logged by an Event Recorder 370. The Decision Logic 360 can use
the context determination to make a decision related to performing
(or not performing) an action based on the determined context. For
example in a particular embodiment, the Decision Logic 360 can
cause the mobile device 330, via the Action Dispatcher 380, to
trigger or configure one or more of the actions described below
based on the determined context: [0025] Device Action 1: If the
user doesn't want to be disturbed during dinner based on a
pre-configured preference, the mobile device 330 can be configured
by the Action Dispatcher 380 to suppress notifications during the
meal or other detected events. [0026] Device Action 2: Because the
determined context is one associated with the user having a meal,
the mobile device 330 can be configured by the Action Dispatcher
380 to set a reminder that is triggered after completion of the
meal. For example, the mobile device 330 can be configured to
automatically remind the user to take his/her medicine after lunch.
[0027] Device Action 3: Based on the sensor data, such as the
accelerometer data and/or microphone input used to detect movement
and/or sounds of the user associated with chewing, the Context
Determination Logic 350 can determine the rate at which the user is
chewing and swallowing. If the user is determined to be swallowing
too quickly based on the user's determined rate in comparison to
pre-stored data corresponding to normative standards for human
chewing and swallowing, the mobile device 330 can be configured by
the Action Dispatcher 380 to issue a notification to the user to
gently coach her/him to slow down and chew/swallow properly. [0028]
Device Action 4: Based on the sensor data and the determination
that the user is eating, the Context Determination Logic 350 can
also determine the times of day and lengths of time when the user
is eating. If the user is determined to be eating for a short
period of time, or intermittently, then the Context Determination
Logic 350 can determine the user is snacking and log the activity
using the Event Recorder 370. Based on the sensor data, such as the
accelerometer data and/or microphone input used to detect movement
author sounds of the user associated with chewing, the Context
Determination Logic 350 can also determine the likely type of food
being consumed (e.g., a crunchy, chewy, or soft food). The Context
Determination Logic 350 can also be configured to prompt the user
to enter information identifying the type of snack they are
consuming. This log can give an accurate calorie consumption
picture for the user over a pre-determined time frame.
[0029] It will be apparent to those of ordinary skill in the art in
view of the disclosure herein that a variety of different actions
can be triggered or configured based on the detection of a context
associated with a user consuming a meal.
[0030] In a second example embodiment, a heart rate monitor or
sensor and/or GSR (galvanic skin response) sensor of
data-generating components 112 in the peripheral device 310 can be
used for detecting stress in the user. The heart rate of the user
as detected by the heart rate sensor can be compared with
pre-stored normative standards of human heart rates. Elevated heart
rates can be indicative of stress. The GSR sensor measures the
electrical conductance of the skin, which can be indicative of
moisture or sweat on the skin. Skin moisture/sweat levels can be
compared with pre-stored normative standards of human skin
moisture/sweat levels. Elevated skin moisture/sweat levels can he
indicative of stress. This data can be used by the Context
Determination Logic 350 to determine if the user is experiencing a
stress episode. The Context Determination Logic 350 can also
determine the timing, length, and severity of the detected stress
episode. This information can be logged using the Event Recorder
370. Additionally, the context determination (e.g., a stress
episode) can be passed from the Context Determination Logic 350 to
the Decision Logic 360. The Decision Logic 360 can use the context
determination to make a decision related to performing (or not
performing) an action based on the determined context. For example
in a particular embodiment, the Decision Logic 360 can cause the
mobile device 330, via the Action Dispatcher 380, to trigger or
configure one or more of the actions described below based on the
determined context: [0031] Device Action 5: Upon the detection of
the user stress episode, the mobile device 330 can be configured by
the Action Dispatcher 380 to issue a notification or warning to the
user and suggest that s/he take a break and relax. The mobile
device 330 can also be configured by the Action Dispatcher 380 to
issue a notification or warning to a third party (e.g., call or
text paramedics) based on the timing, length, and/or severity of
the detected stress episode. The mobile device 330 can also be
configured by the Action Dispatcher 380 to issue a notification or
warning, to a third party based on the detection of the cessation
of heart beat or other events associated with emergency situations
or severe medical conditions. [0032] Device Action 6: Given the
detection of user stress over time, the Context Determination Logic
350 and the Decision Logic 360 can build datasets to enable the
user to look at his/her cumulative stress data and determine stress
patterns, such as the time of the day or the specific tasks being
performed that are producing higher levels of stress in the user.
[0033] Device Action 7: Upon the detection of the user stress
episode, the mobile device 330 can be configured by the. Action
Dispatcher 380 to suppress notifications until the stress level is
reduced. [0034] It will be apparent to those of ordinary skill in
the art in view of the disclosure herein that a variety of
different actions can be triggered or configured based on the
detection of a context associated with a user stress episode or
medical condition.
[0035] In a third example embodiment, a temperature sensor
(thermometer) of generating components components 112 in the
peripheral device 310 can be used for detecting and monitoring the
user's core body temperature in real-time. The user's real-time
body temperature as measured by the thermometer can be compared
with pre-stored normative standards of human body temperature.
Elevated body temperature can be indicative of disease, infection,
stress, or other medical condition. This data can be used by the
Context Determination Logic 350 to determine if the user is
experiencing as medical condition, The Context Determination Logic
350 can also determine the timing, length, and severity of the
detected medical condition. This information can be logged using
the Event Recorder 370. Additionally, the context determination
(e.g., a medical condition) can be passed from the Context
Determination Logic 350 to the Decision Logic 360. The Decision
Logic 360 can use the context determination to make a decision
related to performing (or not performing) an action based on the
determined context. For example in a particular embodiment, the
Decision Logic 360 can cause the mobile device 330, via the Action
Dispatcher 380, to trigger or configure one or more of the actions
described below based on the determined context: [0036] Device
Action 8: Upon the detection of the elevated user body temperature
relative to comparisons with nominal core body temperature data,
the mobile device 330 can be configured by the Action Dispatcher
380 to issue a notification or warning to the user notifying the
user of the presence of slight fevers that may be signs of oncoming
infection. The mobile device 330 can also be configured by the
Action Dispatcher 380 to issue a notification or warning to a third
party (e.g., call or text paramedics) based on the timing, length,
and/or severity of the detected medical condition. The mobile
device 330 can also be configured by the Action Dispatcher 380 to
issue a notification or warning to a third party based on the
detection of the user's core body temperature being above or below
a safe level or other events associated with emergency situations
or severe medical conditions.
[0037] It will be apparent to those of ordinary skill in the art in
view of the disclosure herein that a variety of different actions
can be triggered or configured based on the detection of a context
associated with as user medical condition.
[0038] In a fourth example embodiment, a heart rate monitor or
sensor of data generating components 112 in the peripheral device
310 can be used for detecting the user's mood. The heart rate of
the user as detected by the heart rate sensor can he compared with
pre-stored normative standards of human heart rates associated with
particular moods. Elevated, heart rates can be indicative of
energetic or active moods. Slower heart rates can be indicative of
more mellow or somber moods. This data can he used by the Context
Determination Logic 350 to determine the user's mood. The Context
Determination Logic 350 can also determine the timing, length, and
severity of the detected mood. This information can be logged using
the Event Recorder 370. Additionally, the context determination
(e.g., the user's mood) can be passed from the Context
Determination Logic 350 to the Decision Logic 360. The Decision
Logic 360 can use the context determination to make a decision
related to performing (or not performing) an action based on the
determined context. For example in a particular embodiment, the
Decision Logic 360 can cause the mobile device 330, via the Action
Dispatcher 380, to trigger or configure one or more of the actions
described below based on the determined context: [0039] Device
Action 9: Upon the detection of the user's mood, the mobile device
330 can be configured by the Action Dispatcher 380 to play only
relevant sections of a song to maintain the user's heart rate.
Software in the app 331 running in the mobile device 330 can
analyze a song and determine the song's BPM (beats per minute) at
different sections of the song. Different songs or various portions
of a song can be matched to the current heart rate of the user as
measured by the heart rate monitor in the peripheral device 310.
For example, if the user's heart rate, and thus the user's mood, is
suggestive of music with a pace of 180 BPM, the app 331 can play
only the part of a song where the song's BPM is 180, instead of
playing the complete song. If the target pace is 180 RPM and the
current song has ended, the next song can be played 30 seconds, for
example, from its start to avoid a low tempo beginning, so the user
doesn't slow down and the target pace is maintained. In this
manner, the embodiment can match multimedia content with the
current mood of the consumer.
[0040] It will be apparent to those of ordinary skill in the art in
view of the disclosure herein that a variety of different actions
can be triggered or configured based on the detection of a context
associated with a user's mood.
[0041] In other various embodiments, the sensors of data-generating
components 112 in the peripheral device 310 can be used for
detecting other user contexts. For example, the pressure sensor can
be used to measure atmospheric pressure and thereby infer certain
weather conditions. A user can be notified of rapid changes in
pressure, which may he indicative of the approach of weather
events. In other embodiments, a global positioning system (GPS)
receiver of the data-generating components 112 can be used to
determine the location of the user. For example, the Context
Determination Logic 350 can use GPS data to determine if a user is
currently at work or at a residence. The mobile device 330 can be
configured differently depending on the location of the user. The
GPS data can also be used to determine if the user is stationary or
moving. In other embodiments, image or audio capture devices of the
data-generating components 112 can be used to record audio or video
clips in the proximity of the user. Static images can also be
taken. The recordings can transferred to the app 331 where they can
be parsed and analyzed to extract context information related to
the user's current situation. For example, the audio and image
information can be used to determine that the user is walking in
the city based on traffic noise or images corresponding to city
locations.
[0042] It will be apparent to those of ordinary skill in the art in
view of the disclosure herein that a variety of other well-known
sensing devices or technologies can be included in the sensor
modules added to the peripheral device 310. As such, it will be
apparent to those of ordinary skill in the art in view of the
disclosure herein that a variety of additional user-associated
states and events can be detected and contextually relevant. ac
lions can be taken (or suppressed) in response thereto.
[0043] Referring now to FIG. 10, a processing flow diagram
illustrates an example embodiment of as method for device action
and configuration based on user context detection from sensors in
peripheral devices as described herein. The method 1000 of an
example embodiment includes: receiving, sensor data produced by one
or more sensors in a peripheral device (processing block 1010);
transferring the sensor data to a mobile device for processing
(processing block 1020); determining a context from the sensor data
(processing block 1030); and performing at least one action based
on the determined context, the at least one action including
modifying a configuration in the mobile device for sending
notifications to a use (processing block 1040).
[0044] FIG. 11 shows a diagrammatic representation of a machine in
the example form of a mobile computing and/or communication system
700 within which a set of instructions when executed processing
logic when activated may cause the machine to perform any one or
more of the methodologies described and/or claimed herein. In
alternative embodiments, the machine operates as a standalone
device or may be connected (e.g., networked) to other machines. In
a networked deployment, the machine may operate in the capacity or
a server or a client machine in server-client network environment,
or as a peer machine in a peer-to-peer (or distributed) network
environment. The machine may be a personal computer (PC), a laptop
computer, a tablet computing system, a Personal Digital Assistant
(PDA), a cellular telephone, a smartphone, a web appliance, as
set-top box (STB), a network router, switch or bridge, or any
machine capable of executing a set of instructions (sequential or
otherwise) or activating processing logic that specify actions to
be taken by that machine. Further, while only a single machine is
illustrated, the term "machine" can also be taken to include any
collection of machines that individually or jointly execute a set
(or multiple sets) of instructions or processing logic to perform
any one or more of the methodologies described and/or claimed
herein.
[0045] The example mobile computing and/or communication system 700
includes a data processor 702 (e.g., a System-on-a-Chip (SOC),
general processing core, graphics core, and optionally other
processing logic) and a memory 704, which can communicate with each
other via a bus or other data transfer system 706. The mobile
computing and/or communication system 700 may further include
various input/output (I/O) devices and/or interfaces 710, such as a
touchscreen display, an audio jack, and optionally a network
interface 712. In an example embodiment, the network interface 712
can include one or more radio transceivers configured for
compatibility with any one or more standard wireless and/or
cellular protocols or access technologies (e.g., 2nd (2G), 2.5, 3rd
(3G), 4th (4G) generation, and future generation radio access for
cellular systems, Global System for Mobile communication (GSM),
General Packet Radio Services (GPRS Enhanced Data GSM Environment
EDGE), Wideband Code Division Multiple Access (WCDMA), LTE,
CDMA2000, WLAN, Wireless Router (WR) mesh, and the like). Network
interface 712 may also be configured for use with various other
wired and/or wireless communication protocols, including TCP/IP,
UDP, SIP, SMS, RTP, WAP, CDMA, TDMA, UMTS, UWB, WiFi, WiMax,
Bluetooth, IEEE 802,11x, and the like. In essence, network
interface 712 may include or support virtually any wired and/or
wireless communication mechanisms by which information may travel
between the mobile computing and/or communication system 700 and
another computing or communication system via network 714.
[0046] The memory 704 can represent a machine-readable medium on
which is stored one or more sets of instructions, software,
firmware, or other processing logic (e.g., logic 708) embodying any
one or more of the methodologies or functions described and/or
claimed herein. The logic 708, or a portion thereof, may also
reside, completely or at least partially within the processor 702
during execution thereof by the mobile computing and/or
communication system 700. As such, the memory 704 and the processor
702 may also constitute machine-readable media. The logic 708, or a
portion thereof, may also be configured as processing logic or
logic, at least a portion of which is partially implemented in
hardware. The logic 708, or a portion thereof, may further be
transmitted or received over a network 714 via the network
interface 712. While the machine-readable medium of an example
embodiment can be is single medium, the term "machine-readable
medium" should be taken to include a single non-transitory medium
or multiple non-transitory media (e.g., a centralized or
distributed database, and/or associated caches and computing
systems) that store the one or more sets of instructions. The term
"machine-readable medium" can also be taken to include any
non-transitory medium that is capable of storing, encoding or
carrying a set of instructions fur execution by the machine and
that cause the machine to perform any one or more of the
methodologies of the various embodiments, or that is capable of
storing, encoding or carrying data structures utilized by or
associated with such a set of instructions. The term
"machine-readable medium" can accordingly be taken to include, but
not be limited to, solid-state memories, optical media, and
magnetic media.
[0047] In various embodiments as described herein, example
embodiments include at least the following examples.
[0048] A mobile device comprising: logic, at least a portion of
which is partially implemented in hardware, the logic configured to
determine a context from sensor data and to perform at least one
action based on the determined context, the at least one action
including modifying a configuration in a mobile device for sending
notifications to a user.
[0049] The mobile device as claimed above wherein the sensor data
being encoded with audio signals and received on a microphone line
via a microphone conductor of an audio jack.
[0050] The mobile device as claimed above including a sensor data
receiver to receive sensor data produced by one or more sensors in
a peripheral device and to provide the received sensor data to the
logic for processing.
[0051] The mobile device as claimed above wherein the sensor data
receiver includes a wireless transceiver, the sensor data being
received via a wireless data transmission.
[0052] The mobile device as claimed above wherein the sensor data
is of a type from the group consisting of: biometric data, heart
rate data, temperature data, pressure data, acceleration data,
galvanic skin response data, and global positioning system
data.
[0053] The mobile device as claimed above wherein the mobile device
is a mobile phone.
[0054] A system comprising: a peripheral device including one or
more sensors to produce sensor data; and logic, at least a portion
of which is partially implemented in hardware, the logic configured
to determine a context from the sensor data and to perform at least
one action based on the determined context, the at least one action
including modifying a configuration in a mobile device for sending
notifications to a user.
[0055] The system as claimed above wherein the sensor data being
encoded with audio signals and received on a microphone line via a
microphone conductor of an audio jack.
[0056] The system as claimed above wherein the peripheral device
including a microcontroller coupled to the one or more sensors to
receive the sensor data generated by the one or more sensors, the
microcontroller being further configured to encode the sensor data
into an audio band signal, the peripheral device including an adder
to combine the encoded data with audio signals on the microphone
line, the adder being further configured to transfer the combined
audio signals via the microphone conductor of the audio jack.
[0057] The system as claimed above including a sensor data receiver
to receive the sensor data produced by the one or more sensors in
the peripheral device and to provide the received sensor data to
the logic for processing.
[0058] The system as claimed above wherein the peripheral device
includes a wireless transceiver, the sensor data being sent via a
wireless data transmission.
[0059] The system as claimed, above wherein the sensor data
produced by the one or more sensors in the peripheral device is
biometric data.
[0060] The system as claimed above wherein the sensor data is of a
type from the group consisting of: heart rate data, temperature
data, pressure data, acceleration data, galvanic skin response
data, and global positioning system data.
[0061] The system as claimed above wherein the logic is implemented
in a mobile phone.
[0062] The system as claimed above wherein the peripheral device is
from the group consisting of a headset and an earbud accessory.
[0063] A non-transitory machine-useable storage medium embodying
instructions which, when executed by a machine, cause the machine
to receive sensor data produced by one or more sensors in a
peripheral device; transfer the sensor data to a mobile device for
processing; determine a context from the sensor data; and perform
at least one action based on the determined context, the at least
one action including modifying a configuration in the mobile device
for sending notifications to as user.
[0064] The machine-useable storage medium as claimed above wherein
the instructions being further configured to receive the sensor
data on a microphone line via a microphone conductor of an audio
jack.
[0065] The machine-useable storage medium as claimed above wherein
the instructions being further configured to receive the sensor
data via a wireless data transmission.
[0066] The machine-useable storage medium as claimed above wherein
the sensor data produced by the one or more sensors in the
peripheral device is biometric data.
[0067] The machine-useable storage medium as claimed above wherein
the sensor data is of a type from the group consisting of heart
rate data, temperature data, pressure data, acceleration data
galvanic skin response data, and global positioning system
data.
[0068] A method comprising: determining a context from sensor data;
and performing at least one action based on the determined context,
the at least one action including modifying a configuration in a
mobile device for sending notifications to a user.
[0069] The method as claimed above wherein the sensor data being
encoded with audio signals and received on a microphone line via a
microphone conductor of an audio jack.
[0070] The method as claimed above including receiving sensor data
produced by one or more sensors in a peripheral device and
providing the received sensor data to logic for processing.
[0071] The method as claimed above wherein the sensor data being
received via a wireless data transmission.
[0072] The method as claimed above wherein the sensor data is of a
type from the group consisting of biometric data, heart rate data
temperature data, pressure data, acceleration data, galvanic skin
response data, and global positioning system data.
[0073] The method as claimed above wherein the mobile device is a
mobile phone.
[0074] A mobile apparatus comprising: logic means, at least a
portion of which is partially implemented. In hardware, the logic
means con flamed to determine a context from sensor data and to
perform at least one action based on the determined context, the at
least one action including modifying a configuration in a mobile
device for sending, notifications to a user.
[0075] The mobile apparatus as claimed above wherein the sensor
data being encoded with audio signals and received on a microphone
line via a microphone conductor of an audio jack.
[0076] The mobile apparatus as claimed above including as sensor
data receiving means to receive sensor data produced by one or more
sensors in a peripheral device and to provide the received sensor
data to the logic means for processing.
[0077] The mobile apparatus as claimed above wherein the sensor
data receiving means includes a wireless transceiver, the sensor
data being received via a wireless data transmission.
[0078] The mobile apparatus as claimed above wherein the sensor
data is of a type from the group consisting of: biometric data,
heart rate data, temperature data, pressure data, acceleration
data, galvanic skin response data, and global positioning system
data.
[0079] The mobile apparatus as claimed above wherein the mobile
device is a mobile phone.
[0080] The Abstract of the Disclosure is provided to comply with 37
C.F.R. .sctn.1.72(b), requiring an abstract that will allow the
reader to quickly ascertain the nature of the technical disclosure.
It is submitted with the understanding that it will not be used to
interpret or limit the scope or meaning of the claims. In addition,
in the foregoing Detailed Description, it eau be seen that various
features are grouped together in a single embodiment for the
purpose of streamlining the disclosure. This method of disclosure
is not to be interpreted as reflecting an intention that the
claimed embodiments require more features than are expressly
recited in each claim. Rather, as the following claims reflect,
inventive subject matter lies in less than all features of a single
disclosed embodiment. Thus, the following claims are hereby
incorporated into the Detailed Description, with each claim
standing on its own as a separate embodiment.
* * * * *