Promoting positive activity patterns

• US 10,779,763 B1
• Filed: 12/30/2014
• Issued: 09/22/2020
• 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:
  US14/585,930
• Time in Patent Office: 2,093 Days
• Field of Search: 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. 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 by the one or more processors, implement a method for generating and presenting adaptive activity reminders to a user, via the user interface, for promoting positive activity patterns for the user, the method comprising:

by way of the one or more processors, for each of a plurality of contiguous time intervals spanning a first period of time;

receiving user motion information during the time interval, wherein the user motion information includes motion measurements which represent an ambulation for the user measured using the one or more motion sensors;

determining a time series wherein the time series is based on the motion measurements received during the time interval and based on motion measurements received during previously occurring time intervals within the first period of time;

calculating a quantitative measure of an amount of variation in the ambulation for the time interval based on the time series;

specifying a target threshold of ambulation variation;

comparing the target threshold to the quantitative measure of the amount of variation in the ambulation for the time interval;

based on the comparison, determining that the amount of variation in the ambulation is less than the target threshold;

based at least on the amount of variation in the ambulation being less than the target threshold, generating a pseudorandom value; and

based on the generated pseudorandom value, generating an activity reminder and causing the activity reminder to be presented by way of the user interface on the device.

2. The device of claim 1, wherein the first time period is twenty-four hours, and wherein each of the plurality of contiguous time intervals comprises an ultradian time interval.

3. The device 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.

4. The device of claim 1, wherein calculating the quantitative measure of the amount of variation in the ambulation 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.

5. A system for generating and presenting adaptive activity reminders 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, wherein the computing device uses the one or more processors and one or more memories to;

receive user motion information over a time interval for each of a plurality of time intervals spanning a first time period, wherein the user motion information includes motion measurements which represent an ambulation for the user measured using the one or more wearable motion sensors;

for a first time interval, determine a first time series based on the motion measurements received during the first time interval and the motion measurements 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;

calculate a first quantitative measure of an amount of variation in the ambulation for the first time interval, based on the first time series;

specify a target threshold of ambulation variation;

compare, in a first comparison, the target threshold to the first quantitative measure of the amount of variation in the ambulation;

based at least on the first quantitative measure of the amount of variation in the ambulation being less than the target threshold, generate a pseudorandom value by means of a pseudorandom function;

weight the output of the pseudorandom function such that the pseudorandom value is biased; and

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

6. The system of claim 5, further comprising:

for a second time interval immediately following the first time interval, determining a second time series based on the motion measurements received during the second time interval, the motion measurements received during the first time interval, and the motion measurements received for each time interval of the subset of the plurality of time intervals;

calculating a second quantitative measure of an amount of variation in the ambulation for the second time interval based on the second time series; and

comparing, in a second comparison, the target threshold to the second quantitative measure of the amount of variation in the ambulation;

wherein the pseudorandom value is generated based on each of the first quantitative measure of the amount of variation in the ambulation and the second quantitative measure of the amount of variation in the ambulation being less than the target threshold.

7. The system of claim 5, further comprising:

for a second time interval immediately following the first time interval, determining a second time series based on the motion measurements received during the second time interval, the motion measurements received during the first time interval, and the motion measurements received for each time interval of the subset of the plurality of time intervals;

calculating a second quantitative measure of an amount of variation in the ambulation for the second time interval based on the second time series; and

comparing, in a second comparison, the first quantitative measure of the amount of variation in the ambulation to the second quantitative measure of the amount of variation in the ambulation;

wherein the pseudorandom value is generated based on the first quantitative measure of the amount of variation in the ambulation being less than the target threshold and the second quantitative measure of the amount of variation in the ambulation being less than the first quantitative measure of the amount of variation in the ambulation.

8. The system of claim 5, wherein the first time period is twenty-four hours, and wherein each of the plurality of time intervals spanning the first time period comprises an ultradian time interval.

9. The system of claim 5, 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.

10. A system for generating and presenting adaptive activity reminders 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, wherein the computing device uses the one or more processors and one or more memory devices to, for each of a plurality of contiguous time intervals spanning a first period of time;

receive user motion information during the time interval, wherein the user motion information includes motion measurements which represent an ambulation for the user measured by the one or more motion sensors worn by the user;

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

calculate a quantitative measure of an amount of variation in the ambulation for the time interval based on the time series;

specify a target threshold of ambulation variation;

compare the target threshold to the quantitative measure of the amount of variation in the ambulation for the time interval;

based on the comparison, determine that the amount of variation in the ambulation is less than the target threshold;

based at least on the amount of variation in the ambulation being less than the target threshold, generate a pseudorandom value; and

based on the generated pseudorandom value, generate an activity reminder and cause the activity reminder to be presented by way of the user interface of the computing device.