TIME OUT-OF-HOME MONITORING

• US 20160171378A1
• Filed: 12/15/2015
• Published: 06/16/2016
• Est. Priority Date: 12/15/2014
• Status: Abandoned Application

• Current Assignee: Oregon Health & Science University
• Original Assignee: Oregon Health & Science University
• Inventors: Petersen, Johanna, Austin, Daniel, Kaye, Jeffrey, Pavel, Misha, Hayes, Tamara
• Application Number:
  US14/969,122
• Publication Number:
  US 20160171378A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Systems and methods for determining time a subject spends outside of their home based on signals received from unobtrusive sensors in the home are disclosed. In one example approach, a method for determining time a subject spends outside of their home comprises receiving sensor firing data for a duration from sensors in the home, dividing the duration into epochs, extracting features from the sensor firing data received during the epoch, and classifying each epoch as out-of-home or in-home based on the features extracted from the sensor firing data during the epoch.

Claims

1. A computer-implemented method for determining time a subject spends outside of their home, comprising:

receiving sensor firing data for a duration from sensors in the home, the sensors comprising an exterior door sensor and room motion sensors;

dividing the duration into epochs;

for each epoch, extracting features from the sensor firing data received during the epoch, wherein extracting features from the sensor firing data received during the epoch comprises;

calculating a number of sensor firings during the epoch;

detecting whether the subject was in or out of a bed during the epoch based on sensor firing data from room motion sensors;

in response to the exterior door sensor being the last sensor fired during the epoch, indicating a leaving event during the epoch;

in response to the exterior door sensor being the first sensor fired during the epoch, indicating an arrival event during the epoch;

identifying whether or not the last motion sensor fired during the epoch occurred in a room of the home from which the subject could directly leave the home; and

classifying, using a classifier, each epoch as out-of-home or in-home based on the features extracted from the sensor firing data during the epoch.

2. The method of claim 1, wherein extracting features from the sensor firing data received during the epoch further comprises calculating forward and/or backward lags of one or more of the extracted features.

3. The method of claim 1, wherein extracting features from the sensor firing data received during the epoch further comprises calculating forward and/or backward lags of one or more of

the number of sensor firings during the epoch, the last sensor fired during the epoch, and the first sensor fired during the epoch.

4. The method of claim 1, wherein the classifier comprises a binary classifier.

6. The method of claim 1, wherein classifying each epoch as out-of-home or in-home comprises calculating a probability that the epoch is out-of-home or in-home.

7. The method of claim 6, wherein an epoch is classified as out-of-home if the calculated probability of the epoch is greater than 0.5 and the epoch is classified as in-home if the calculated probability of the epoch is less than 0.5.

8. The method of claim 1, wherein the classifier is trained on a training set to generate model parameters estimated by maximum likelihood.

9. The method of claim 1, further comprising estimating an amount of time the subject is out of the home based on the classification of the epochs.

10. The method of claim 9, further comprising generating a loneliness score based on the estimated amount of time the subject is out of the home, wherein a decreasing amount of time the subject is out of the home is correlated with an increasing loneliness score.

11. The method of claim 9, further comprising indicating a risk of loneliness in response to the estimated amount of time out of the home less than a threshold.

12. The method of claim 1, wherein the duration comprises a plurality of days and wherein the method further comprises generating a distribution of probabilities that the subject is out of the home during different times of the day.

13. The method of claim 1, further comprising removing consecutive door sensor firing data having the same signals prior to extracting features from the sensor firing data.

14. The method of claim 1, wherein the exterior door sensor comprises a contact sensor.

15. The method of claim 14, wherein the contact sensor comprises a magnetic contact sensor.

16. The method of claim 1, wherein the room motion sensors comprise infrared motion sensors.

17. The method of claim 1, further comprising calculating a variability in the amount of time the subject is out of the home and indicating a health state of the subject based on the variability.

18. A system for monitoring time a subject spends outside of their home, comprising:

one or more room motion sensors positioned in rooms of the home;

an exterior door sensor coupled to an exterior door of the home; and

a computing device in communication with each sensor, the computing device comprising;

a logic subsystem; and

a data-holding subsystem holding instructions executable by the logic subsystem to perform the steps of claim 1.

19. The system of claim 18, wherein the exterior door sensor comprises a contact sensor.

20. The system of claim 19, wherein the contact sensor comprises a magnetic contact sensor.

21. The system of claim 18, wherein the room motion sensors comprise infrared motion sensors.

22. The system of claim 18, wherein the one or more motion sensors comprise a motion sensor in each room of the home.

5. The method 4, wherein the classifier uses logistic regression to classify each epoch.