Sedentary Period Detection Utilizing a Wearable Electronic Device

• US 20160203691A1
• Filed: 03/23/2016
• Published: 07/14/2016
• Est. Priority Date: 01/15/2013
• Status: Active Grant

• Current Assignee: Fitbit, Inc. (Alphabet Inc.)
• Original Assignee: Fitbit, Inc. (Alphabet Inc.)
• Inventors: Russell, Allison Maya, Wasson, Zachariah Lord II, Arnold, Jacob Antony, Yuen, Shelten Gee Jao
• Granted Patent: US 9,728,059 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0209 adapted for power saving
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/222 combined with detection or measurement of physiological parameters, e.g. heart rate
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/743 Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G01C 22/006 Pedometers
• CPC Class Codes: G08B 21/0415 detecting absence of activity per se
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/40 : relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
• CPC Class Codes: G16H 40/63 : for local operation

Abstract

Systems and methods for determining a sedentary state of a user are described. Sensor data is collected and analyzed to calculate metabolic equivalent of task (MET) measures for a plurality of moments of interest. Based on the MET measures and a time period for which the MET measures exceed a threshold value, it is determined whether the user is in a sedentary state. If the user is in the sedentary state, the user is provided a notification to encourage the user to perform a non-sedentary activity.

Claims

1. A method comprising:

receiving by a server samples of sensor information obtained from a wearable electronic device for a plurality of moments of interest, the sensor information being generated when a user associated with the wearable electronic device performs one or more activities, the samples being metabolic equivalent of task measures that are normalized;

classifying by a processor of the server the samples for each of the moments of interest into at least one of a sedentary status and a non-sedentary status, based on comparing the samples for each of the moments of interest to a predetermined threshold;

detecting by the processor a time period for which a number of consecutive ones of the samples indicate the sedentary status;

determining by the processor whether the time period is greater than a threshold time period; and

identifying by the processor that the user is in a sedentary state upon determining that the time period is greater than the threshold time period.

2. The method of claim 1, further comprising:

generating an electronic notification in response to identifying that the user is in the sedentary state; and

sending from the server via a computer network to the wearable electronic device the electronic notification for presentation of the electronic notification on the wearable electronic device, wherein the electronic notification includes a message recommending the user to end the sedentary state.

3. The method of claim 1, further comprising:

determining that a value of one of the samples for one of the moments of interest is less than the predetermined threshold; and

classifying the one of the samples as having the sedentary status responsive to determining that the value is less than the predetermined threshold.

4. The method of claim 1, wherein each of the samples is classified as having the sedentary status, or the non-sedentary status, or an asleep status.

5. The method of claim 1, wherein each of the samples is classified as having the sedentary status, or the non-sedentary status, or a status indicating that the user is not wearing the wearable electronic device.

6. The method of claim 1, further comprising:

detecting by the processor a time period for which a number of consecutive ones of the samples indicate the non-sedentary status;

determining by the processor that the time period for which the number of consecutive ones of the samples indicate the non-sedentary status is greater than a pre-determined time period; and

identifying by the processor that the user is in a non-sedentary state upon determining that the time period for which the number of consecutive ones of the samples indicate the non-sedentary status is greater than the pre-determined time period.

7. The method of claim 6, further comprising:

determining by the processor that the time period for which the number of consecutive ones of the samples indicate the non-sedentary status is contiguous with the time period for which the number of consecutive ones of the samples indicate the sedentary status;

identifying by the processor that the user has transitioned from the sedentary state to the non-sedentary state upon determining that the time period for which the number of consecutive ones of the samples indicate the non-sedentary status is contiguous with the time period for which the number of consecutive ones of the samples indicate the sedentary status; and

sending from the server via a computer network to the wearable electronic device an electronic notification for display via the wearable electronic device upon identifying that the user has transitioned into the non-sedentary state, the electronic notification including a motivational message.

8. The method of claim 1, wherein the metabolic equivalent of task measures are measures of energy expenditure, wherein each of the metabolic equivalent of task measures is non-zero.

9. The method of claim 1, further comprising:

determining that a value of one of the samples for one of the moments of interest is greater than the predetermined threshold; and

classifying the one of the samples as having the non-sedentary status responsive to determining that the value is greater than the predetermined threshold.

10. The method of claim 1, further comprising:

determining that a value of one of the samples for one of the moments of interest is greater than a second predetermined threshold higher than the predetermined threshold; and

classifying the one of the samples as having the non-sedentary status responsive to determining that the value is greater than the second predetermined threshold.

11. The method of claim 1, further comprising:

determining that a value of a specific one of the samples for a specific moment of interest is greater than the predetermined threshold and less than a second predetermined threshold, the second predetermined threshold being higher than the predetermined threshold;

determining that a value of one of the samples for a preceding moment of interest preceding the specific moment of interest corresponds to the sedentary status;

determining that a value of one of the samples for a subsequent moment of interest subsequent to the specific moment of interest corresponds to the sedentary status; and

classifying the specific one of the samples as having the sedentary status.

12. The method of claim 1, further comprising:

determining that values of samples for a group of consecutive moments of interest are each greater than the predetermined threshold and less than a second predetermined threshold, the second predetermined threshold being higher than the predetermined threshold;

determining that a value of one of the samples for a preceding moment of interest preceding the group of consecutive moments of interest corresponds to the sedentary status;

determining that a value of one of the samples for a subsequent moment of interest subsequent to the group of consecutive moments of interest corresponds to the sedentary status; and

classifying each of the samples for the group of consecutive moments of interest as having the sedentary status.

13. The method of claim 12, further comprising:

prior to the classifying each of the samples for the group of consecutive moments of interest as having the sedentary status, determining that a size of the group of consecutive moments of interest is less than a size threshold.

14. The method of claim 1, wherein the moments of interest occur at regular time intervals.

15. A method comprising:

receiving by a server samples of sensor information obtained from a wearable electronic device for a plurality of moments of interest, the sensor information being generated when a user associated with the wearable electronic device performs one or more activities, the samples being metabolic equivalent of task measures that are normalized;

classifying by a processor of the server a specific one of the samples for a specific one of the moments of interest into a sedentary status, based on determining that a value of the specific one of the samples is between a first threshold value and a second threshold value, and that the specific one of the moments of interest is preceded by a first moment of interest for which a status of the user is classified as sedentary, and that the specific one of the moments of interest is followed by a second moment of interest for which the status of the user is classified as sedentary;

detecting by the processor a time period for which a number of consecutive ones of the samples indicate the sedentary status;

determining by the processor whether the time period is greater than a threshold time period; and

identifying by the processor that the user is in a sedentary state upon determining that the time period is greater than the threshold time period.

16. A system comprising:

a communication device configured to receive samples of sensor information obtained from a wearable electronic device for a plurality of moments of interest, the sensor information being generated when a user associated with the wearable electronic device performs one or more activities, the samples being metabolic equivalent of task measures that are normalized; and

a processor coupled to the communication device, the processor configured to classify the samples for each of the moments of interest into at least one of a sedentary status and a non-sedentary status, based on comparing the samples for each of the moments of interest to a predetermined threshold,

the processor configured to detect a time period for which a number of consecutive ones of the samples indicate the sedentary status;

the processor configured to determine whether the time period is greater than a threshold time period; and

the processor configured to identify that the user is in a sedentary state upon determining that the time period is greater than the threshold time period.

17. The system of claim 16, wherein the processor is further configured to generate an electronic notification in response to identifying that the user is in the sedentary state,

wherein the communication device is configured to send via a computer network to the wearable electronic device the electronic notification for presentation of the electronic notification on the wearable electronic device, wherein the electronic notification includes a message recommending the user to end the sedentary state.

18. The system of claim 16, wherein the processor is further configured to determine that a value of one of the samples for one of the moments of interest is less than the predetermined threshold, wherein the processor is further configured to classify the one of the samples as having the sedentary status responsive to determining that the value is less than the predetermined threshold.

19. The system of claim 16, wherein each of the samples is classified as having the sedentary status, the non-sedentary status, or an asleep status.

20. The system of claim 16, wherein each of the samples is classified as having the sedentary status, the non-sedentary status, or a status indicating that the user is not wearing the wearable electronic device.

21. The system of claim 16, wherein the processor is configured to:

detect a time period for which a number of consecutive ones of the samples indicate the non-sedentary status;

determine that the time period for which the number of consecutive ones of the samples indicate the non-sedentary status is greater than a pre-determined time period; and

identify that the user is in a non-sedentary state upon determining that the time period for which the number of consecutive ones of the samples indicate the non-sedentary status is greater than the pre-determined time period.

22. The system of claim 16, wherein the processor is configured to determine that the time period for which the number of consecutive ones of the samples indicate the non-sedentary status is contiguous with the time period for which the number of consecutive ones of the samples indicate the sedentary status,

wherein the processor is further configured to identify that the user has transitioned from the sedentary state to the non-sedentary state upon determining that the time period for which the number of consecutive ones of the samples indicate the non-sedentary status is contiguous with the time period for which the number of consecutive ones of the samples indicate the sedentary status,

wherein the communication device is configured to send via a computer network to the wearable electronic device an electronic notification for display via the wearable electronic device upon determining that the user has transitioned into the non-sedentary state, the electronic notification including a motivational message.

23. The system of claim 16, wherein the metabolic equivalent of task measures are measures of energy expenditure, wherein each of the metabolic equivalent of task measures is non-zero.

24. The system of claim 16, wherein the processor is configured to:

determine that a value of one of the samples for one of the moments of interest is greater than the predetermined threshold; and

classify the one of the samples as having the non-sedentary status responsive to determining that the value is greater than the predetermined threshold.

25. The system of claim 16, wherein the processor is configured to:

determine that a value of one of the samples for one of the moments of interest is greater than a second predetermined threshold higher than the predetermined threshold; and

classify the one of the samples as having the non-sedentary status responsive to determining that the value is greater than the second predetermined threshold.

26. The system of claim 16, wherein the processor is configured to:

determine that a value of a specific one of the samples for a specific moment of interest is greater than the predetermined threshold and less than a second predetermined threshold, the second predetermined threshold being higher than the predetermined threshold;

determine that a value of one of the samples for a preceding moment of interest preceding the specific moment of interest corresponds to the sedentary status;

determine that a value of one of the samples for a subsequent moment of interest subsequent to the specific moment of interest corresponds to the sedentary status; and

classify the specific one of the samples as having the sedentary status.

27. The system of claim 16, wherein the processor is configured to:

determine that values of samples for a group of consecutive moments of interest are each greater than the predetermined threshold and less than a second predetermined threshold, the second predetermined threshold being higher than the predetermined threshold;

determine that a value of one of the samples for a preceding moment of interest preceding the group of consecutive moments of interest corresponds to the sedentary status;

determine that a value of one of the samples for a subsequent moment of interest subsequent to the group of consecutive moments of interest corresponds to the sedentary status; and

classify each of the samples for the group of consecutive moments of interest as having the sedentary status.

28. The system of claim 27, wherein the processor is configured to:

determine that a size of the group of consecutive moments of interest is less than a size threshold prior to the classifying each of the samples for the group of consecutive moments of interest as having the sedentary status.

29. The system of claim 16, wherein the moments of interest occur at regular time intervals.

30. A system comprising:

a communication device configured to receive samples of sensor information obtained from a wearable electronic device for a plurality of moments of interest, the sensor information being generated when a user associated with the wearable electronic device performs one or more activities, the samples being metabolic equivalent of task measures that are normalized;

a processor coupled to the communication device, the processor configured to classify a specific one of the samples for a specific one of the moments of interest into a sedentary status, based on a determination that a value of the specific one of the samples is between a first threshold value and a second threshold value, and that the specific one of the moments of interest is preceded by a first moment of interest for which a status of the user is classified as sedentary, and that the specific one of the moments of interest is followed by a second moment of interest for which the status of the user is classified as sedentary,

the processor configured to detect a time period for which a number of consecutive ones of the samples indicate the sedentary status,

the processor configured to determine whether the time period is greater than a threshold time period,

the processor configured to identify that the user is in a sedentary state upon determining that the time period is greater than the threshold time period.