Cadence spectra for health and fitness promotion

• US 10,546,510 B1
• Filed: 10/06/2014
• Issued: 01/28/2020
• Est. Priority Date: 10/04/2013
• Status: Active Grant

• Current Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Original Assignee: Cerner Innovation, Inc. (Oracle Corporation)
• Inventors: McNair, Douglas S.
• Primary Examiner(s): Utama, Robert J
• Application Number:
  US14/507,325
• Time in Patent Office: 1,940 Days
• Field of Search: 434247, 434262
• CPC Class Codes: A61B 2560/0214 of power generation or supply
• CPC Class Codes: A61B 2560/0456 Apparatus provided with a docking unit
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• 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: G08B 21/24 Reminder alarms, e.g. anti-loss alarms
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G09B 19/0092 Nutrition

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 measuring physical activity patterns during the waking hours of a human, and in some embodiments continuously measuring these activity patterns; automatically ascertaining whether the patterns exhibit sufficient frequency and variability of activity such as confers certain health benefits; and 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. A device comprising one or more motion sensors, a user interface, one or more processors, and one or more memory devices having computer-executable instructions embodied thereon, that, when executed, implement a method for generating and presenting adaptive activity alerts to a user, via the user interface, for promoting positive activity patterns for the user, the method comprising:

receiving, from the one or more motion sensors, a first set of user-motion information over a first time interval, wherein the first set of user-motion information corresponds to motion of the user over the first time interval;

based on the received first set of user-motion information, determining a first level of variability of activity during the first time interval, wherein determining the first level of variability of activity comprises determining a power spectral density of the first set of user-motion information;

specifying a target threshold of activity variability, wherein the target threshold of activity variability comprises a target power spectral density;

comparing, in a first comparison, the target threshold of activity variability to the first level of variability of activity during the first time interval;

based on the first comparison, determining that the first level of variability of activity is less than the target threshold of activity variability;

based at least on the first level of variability of activity being less than the target threshold of activity variability, generating a pseudorandom value; and

based on the generated pseudorandom value, generating an activity alert and presenting the activity alert by way of the user interface on the device.

2. The device 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, determining a second level of variability of activity during the second time interval; and

comparing, in a second comparison, the target threshold of activity variability to the second level of variability of activity during the second time interval;

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

3. The device of claim 2, wherein the first time interval immediately precedes the second time interval.

4. The device of claim 1, wherein the pseudorandom value represents logically true or false, and wherein determining to provide the activity alert comprises providing the activity alert when the pseudorandom value represents logically true, and not providing the activity alert when the pseudorandom value represents logically false.

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

6. The device of claim 1, wherein the activity alert comprises a report provided to a health care provider.

7. The device of claim 1 further comprising:

responsive to a provided activity alert, 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, determining a third level of variability of activity during the third time interval; and

storing the determined third level of variability of activity.

8. The device of claim 1, wherein the user-motion information comprises ambulatory information for a user.

9. The device of claim 1, wherein determining the first level of variability of activity comprises determining 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 device of claim 2, wherein the first and second time intervals comprise ultradian time intervals between 10 and 30 minutes.

20. The device of claim 1, wherein the device is a wearable device.

11. A system for generating and presenting adaptive activity alerts to a user for promoting positive activity patterns for the user, the system comprising:

one or more wearable motion sensors configured to measure user motion;

a computing device comprising one or more processors, one or more memory devices, and a user interface, the computing device configured to, for each of a plurality of contiguous time intervals spanning a first period of time;

receive user-motion information from the one or more wearable motion sensors during the time interval;

determine a time series, wherein the time series is based on the user-motion information received during the time interval and based on user-motion information received during previously occurring time intervals within the first period of time;

determine a level of activity variability for the time interval based on the time series, wherein determining the level of activity variability comprises determining a power spectral density of the user motion for the time series;

specify a target threshold of activity variability, wherein the target threshold of activity variability comprises a target power spectral density;

compare the target threshold of activity variability to the level of activity variability for the time interval;

based on comparison, determine that the level of activity variability is less than the target threshold of activity variability;

based at least on the level of activity being less than the target threshold of activity variability, generate a pseudorandom value; and

based on the generated pseudorandom value, generate an activity alert and presenting the activity alert by way of the user interface on the computing device.

12. The system of claim 11, wherein the first time period is approximately twenty-four hours, and wherein each of the plurality of contiguous time intervals comprises an ultradian time interval.

13. The system of claim 11, wherein the pseudorandom value represents logically true or false;

wherein determining to provide the activity alert comprises providing the notice when the pseudorandom value represents logically true, and not providing the notice when the pseudorandom value represents logically false; and

wherein determining the level of activity variability comprises determining one of Shannon, Jeffreys, Laplace, Minimax, Tsallis, Chao-Shen, Dirichlet, Miller-Madow, James-Stein Shrinker, or Nemenman-Shafee-Bialek (NSB) entropy, or RMS SD variability.

14. A system for generating and presenting adaptive activity alerts to a user for promoting positive activity patterns for the user, the system comprising:

one or more wearable motion sensors configured to measure user motion;

one or more processors;

a user feedback interface;

one or more memory devices having computer-executable instructions embodied thereon, that, when executed, implement the following operations;

receive user-motion information from the one or more wearable motion sensors over a time interval for each of a plurality of time intervals spanning a first time period;

for a first time interval, determine a first time series based on user-motion information received during the first time interval and user-motion information received during each time interval of a subset of the plurality of time intervals, the subset of time intervals occurring within the first time period and before the first time interval;

determine a first amount of activity variability for the first time interval, based on the first time series, wherein determine the first amount of activity variability comprises determine a power spectral density of the user motion for the first time series;

specify a target threshold of activity variability, wherein the target threshold of activity variability comprises a target power spectral density;

compare, in a first comparison, the target threshold of activity variability to the first amount of activity variability;

based at least on the first amount of activity variability being less than the target threshold of activity variability, generate a pseudorandom value; and

based on the generated pseudorandom value, generate an activity alert and presenting the activity alert by way of the user feedback interface.

15. The system of claim 14, the operations further comprising:

for a second time interval immediately following the first time interval, determine a second time series based on user-motion information received during the second time interval, the user-motion information received during the first time interval, and user-motion received for each time interval of the subset of the plurality of time intervals;

determine a second amount of activity variability for the second time interval based on the second time series; and

compare, in a second comparison, the target threshold of activity variability to the second amount of activity variability;

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

16. The system of claim 14, the operations further comprising:

for a second time interval immediately following the first time interval, determine a second time series based on user-motion information received during the second time interval, the user-motion information received during the first time interval, and the user-motion received for each time interval of the subset of the plurality of time intervals;

determine a second amount of activity variability for the second time interval based on the second time series; and

compare, in a second comparison, the first amount of activity variability to the second amount of activity variability;

wherein the pseudorandom value is generated based on the first amount of activity variability being less than the target threshold of activity variability and the second amount of activity variability being less than the first amount of activity variability.

17. The system of claim 14, wherein the first time period is approximately twenty-four hours, and wherein each of the plurality of time intervals spanning the first time period comprises an ultradian time interval.

18. The system of claim 15, wherein determine the first amount of variability of activity comprises determine the power spectral density computed by one or more of Fast Fourier Transform, Welch averaged Fourier transform, Wavelet Transform, or Hadamard transform.

19. The system of claim 18 wherein the first and second time intervals comprise ultradian time intervals between 5 and 32 minutes.