Promoting positive activity patterns

• US 10,420,486 B1
• Filed: 01/28/2013
• Issued: 09/24/2019
• Est. Priority Date: 01/27/2012
• Status: Active Grant

• Current Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Original Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Inventors: McNair, Douglas S.
• Primary Examiner(s): Cerioni, Daniel L
• Application Number:
  US13/751,961
• Time in Patent Office: 2,430 Days
• Field of Search: 600587, 600595
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/1113 Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/6802 Sensor mounted on worn items
• CPC Class Codes: A61B 5/6887 mounted on external non-worn devices, e.g. non-medical devices
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/72 Signal processing specially adapted for physiological signals or for diagnostic purposes character recognition, recognising digital ink, document-oriented image-based pattern recognition G06V30/00; recognition or understanding of postal images G06V30/424
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• 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 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/7405 using sound
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7455 characterised by tactile indication, e.g. vibration or electrical stimulation
• 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: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Methods, computer systems, and computer readable media are provided for promoting positive activity patterns for users and facilitate long-term adherence to the activity patterns, such as by providing alerts or electronic reminders to ambulate in a fashion that is responsive to an individual'"'"'s actual activity patterns and behaviors and compatible with routine activities in the workplace and home. In particular, embodiments of the present invention are directed to (1) measuring physical activity patterns during the waking hours of a human, and in some embodiments continuously measuring these activity patterns; (2) automatically ascertaining whether the patterns exhibit sufficient frequency and variability of activity such as confers certain health benefits; and (3) if the patterns do not manifest such features, to adaptively provide sensible reminders at irregular within-day intervals such as are likely to establish healthy patterns of ambulation and other light activity.

Claims

1. Non-transitory computer-readable media having computer-executable instructions embodied thereon, that, when executed by one or more processors, implement a method of generating and providing adaptive activity reminders to a user, via a user device, for promoting positive activity patterns for the user, the method comprising:

receiving on a computing device a first set of user-motion information over a first time interval, the first set of user-motion information determined from one or more sensors positioned at one or more locations on the user, wherein the first set of user-motion information includes user-motion measurements obtained over the first time interval which correspond to a first ambulation for the user during the first time interval;

calculating a first entropy for an amount of variation in the first ambulation based on the user-motion measurements, wherein the first entropy is a quantitative measure of the amount of variation for the first ambulation for the user during the first time interval;

specifying a target entropy threshold amount of ambulation variation;

comparing, in a first comparison, the target entropy threshold to the first entropy of the first ambulation during the first time interval;

based on the first comparison, determining whether the first entropy of the first ambulation is less than the target entropy threshold;

when the first entropy of the first ambulation is less than the target entropy threshold, then generating a pseudorandom value;

based on the generated pseudorandom value, determining a pseudorandom interval; and

at the pseudorandom interval, generating an activity reminder and presenting the activity reminder by way of a user interface on the user device, wherein the presented activity reminder prompts the user to perform a physical activity that increases an entropy level of user activity.

2. The computer-readable media of claim 1, further comprising:

receiving a second set of user-motion information over a second time interval;

based on the received second set of user-motion information, calculating a second entropy for a second ambulation for the user during the second time interval; and

comparing, in a second comparison, the target entropy threshold to the second entropy for the second ambulation;

wherein the pseudorandom value is generated based on each of the first entropy level for the first ambulation and the second entropy level for the second ambulation being less than the target entropy threshold.

3. The computer-readable media of claim 2, wherein the first time interval immediately precedes the second time interval.

4. The computer-readable media of claim 1, wherein the pseudorandom value represents logically true or false, and wherein presenting the activity reminder comprises presenting the activity reminder when the pseudorandom value represents logically true, and not presenting the activity reminder when the pseudorandom value represents logically false.

5. The computer-readable media of claim 1, wherein the activity reminder comprises at least one of an audible alert, a haptic alert, a displayed message, a displayed visual indicator, or a text message.

6. The computer-readable media of claim 1, wherein the activity reminder comprises a report provided to a health care provider.

7. The computer-readable media of claim 1 further comprising:

responsive to a provided activity reminder, receiving a third set of user-motion information over a third time interval succeeding the first time interval;

based on the received third set of user-motion information, calculating a third entropy for a third ambulation for the user during the third time interval; and

storing the third entropy for the third ambulation for the user.

8. The computer-readable media of claim 5 wherein the activity reminder is provided for at least 10 seconds.

9. The computer-readable media of claim 1, wherein calculating the first entropy of the first ambulation comprises calculating one of Shannon, Jeffreys, Laplace, Minimax, Tsallis, Chao-Shen, Dirichlet, Miller-Madow, James-Stein Shrinker, or Nemenman-Shafee-Bialek (NSB) entropy, or RMSSD variability.

10. The computer-readable media of claim 2 wherein the first and second time intervals comprise ultradian time intervals between 10 and 30 minutes.

11. A monitoring device that provides adaptive activity reminders for promoting positive activity patterns for a user, comprising:

a user interface;

one or more computing devices that;

(1) receive a first set of user-motion information over a first time interval, the first set of user-motion information determined from one or more sensor circuits, wherein the first set of user-motion information includes user-motion measurements obtained over the first time interval which correspond to a first ambulation for the user during the first time interval;

(2) calculate a first entropy for a first activity pattern based on the user-motion measurements, wherein the first entropy is a quantitative measure of the amount of variation in a level of activity for the first activity pattern for the user during the first time interval;

(3) specify a target entropy threshold amount of variation;

(4) compare, in a first comparison, the target entropy threshold to the first entropy of the first activity pattern during the first time interval;

(5) based on the first comparison, determine whether the first entropy of the first activity pattern is less than the target entropy threshold;

(6) when the first entropy of the first activity pattern is less than the target entropy threshold, then generate a pseudorandom value;

(7) based on the generated pseudorandom value, determine a pseudorandom interval; and

(8) at the pseudorandom interval, generate an activity reminder and present the activity reminder by way of the user interface, wherein the presented activity reminder prompts the user to perform a physical activity associated with a positive activity pattern.

12. The monitoring device of claim 11, wherein the one or more computing devices further:

receive a second set of user-motion information over a second time interval;

based on the received second set of user-motion information, calculate a second entropy for a second activity pattern for the user during the second time interval; and

compare, in a second comparison, the target entropy threshold to the second entropy for the second activity pattern;

wherein the pseudorandom value is generated based on each of the first entropy for the first activity pattern and the second entropy for the second activity pattern being less than the target entropy threshold.

13. The monitoring device of claim 12, wherein the first time interval immediately precedes the second time interval.

14. The monitoring device of claim 11, wherein the pseudorandom value represents logically true or false, and wherein presenting the activity reminder comprises presenting the activity reminder when the pseudorandom value represents logically true, and not presenting the activity reminder when the pseudorandom value represents logically false.

15. The monitoring device of claim 11, wherein the activity reminder comprises at least one of an audible alert, a haptic alert, a displayed message, a displayed visual indicator, or a text message.

16. The monitoring device of claim 11, wherein the activity reminder comprises a report provided to a health care provider.

17. The monitoring device of claim 11 further comprising:

responsive to a provided activity reminder, receiving a third set of user-motion information over a third time interval succeeding the first time interval;

based on the received third set of user-motion information, calculating a third entropy for a third activity pattern for the user during the third time interval; and

storing the third entropy for the third activity pattern for the user.

18. The monitoring device of claim 15 wherein the activity reminder is provided for at least 10 seconds.

19. Non-transitory computer-readable media having computer-executable instructions embodied thereon, that, when executed by one or more processors, implement a method of generating and providing adaptive activity reminders to a user, via a user device, for promoting positive activity patterns for the user, the method comprising:

receiving on a computing device a first set of user-motion information over a first time interval, the first set of user-motion information determined from one or more sensors, wherein the first set of user-motion information includes user-motion measurements obtained over the first time interval which correspond to a first ambulation for the user during the first time interval;

calculating a first entropy for an amount of variation in the first ambulation based on the user-motion measurements, wherein the first entropy is a quantitative measure of the amount of variation for the first ambulation for the user during the first time interval;

specifying a target entropy threshold of ambulation variation;

comparing, in a first comparison, the target entropy threshold to the first entropy of the first ambulation during the first time interval;

based on the first comparison, determining whether the first entropy of the first ambulation is less than the target entropy threshold;

when the first entropy of the first ambulation is less than the target entropy threshold, then generating a pseudorandom value, wherein the pseudorandom variable is generated based as least in part on a linear feedback shift register;

based on the generated pseudorandom value, determining a pseudorandom interval; and

at the pseudorandom interval, generating an activity reminder and presenting the activity reminder by way of a user interface on the user device, wherein the presented activity reminder prompts the user to perform a physical activity associated with a positive activity pattern.