INTEGRATED SENSOR NETWORK METHODS AND SYSTEMS

• US 20100302043A1
• Filed: 06/01/2010
• Published: 12/02/2010
• Est. Priority Date: 06/01/2009
• Status: Active Grant

• Current Assignee: Curators of the University of Missouri (University of Missouri)
• Original Assignee: Curators of the University of Missouri (University of Missouri)
• Inventors: Florea, Elena, Wang, Shuang, Sledge, Isaac J., Guevara, Rainer Dane A., Popescu, Mihail, Skubic, Marjorie, Keller, James M., Rantz, Marilyn J.
• Granted Patent: US 10,188,295 B2
• Time in Patent Office: Days
• US Class Current: 340/573.1
• CPC Class Codes: A61B 2503/08 Elderly
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• 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 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• 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 5/7264 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: G08B 21/0423 detecting deviation from an expected pattern of behaviour or schedule
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Methods and systems for an integrated sensor network are described. In one embodiment, sensor data may be accessed from a plurality of motion sensors and a bed sensor deployed in a living unit for a first time period. An activity pattern for the first time period may be identified based on at least a portion of sensor data associated with the first time period. The activity pattern may represent a physical and cognitive health condition of a person residing in the living unit. Additional sensor data may be accessed from the motion sensors and the bed sensor deployed for a second time period. A determination of whether a deviation of the activity pattern of the first time period has occurred for the second time period may be performed. An alert may be generated based on a determination that the derivation has occurred. In some embodiments, user feedback is captured on the significance of the alerts, and the alert method is customized based on this feedback. Additional methods and systems are disclosed.

Claims

1. A method comprising:

accessing sensor data from a plurality of motion sensors and a bed sensor deployed in a living unit for a first time period;

identifying an activity pattern for the first time period based on at least a portion of sensor data associated with the first time period, the activity pattern representing a physical and cognitive health condition of a person residing in the living unit;

accessing additional sensor data from the plurality of motion sensors and the bed sensor deployed in the living unit for a second time period, the second time period occurring after the first time period;

determining whether a deviation of the activity pattern of the first time period has occurred for the second time period; and

generating an alert based on a determination that the derivation has occurred.

2. The method of claim 1, wherein the activity pattern includes a plurality of features and the deviation is associated with a particular feature of the plurality of features.

3. The method of claim 1, further comprising:

identifying the activity pattern for the second time period based on access of the additional sensor data associated with the second time period,

wherein determining whether the deviation has occurred includes determining whether the deviation of the activity pattern of the second time period from the activity pattern of the first time period exceeds a threshold

4. The method of claim 1, further comprising:

analyzing health data associated with the person,

wherein generation of the alert is based on when the activity pattern of the second time period from the activity pattern of the first time period exceeds the threshold and analysis of the health data.

6. The method of claim 1, further comprising:

accessing the sensor data from a stove sensor deployed in the living unit; and

accessing the additional sensor data from the stove sensor deployed in the living unit.

7. The method of claim 1, wherein the plurality of motion sensors and the bed sensor are passive, non-wearable sensors.

8. The method of claim 1, further comprising:

identifying at least a portion of the sensor data associated with the first time period as being associated with the person,

wherein identification of the activity pattern for the first time period is based on at least the portion of sensor data associated with the first time period associated with the person.

9. The method of claim 1, wherein the first period of time has a same time duration as the second period of time.

10. The method of claim 1, further comprising:

calculating statistical parameters of at least a portion of the sensor data for the first time period,

wherein determining whether the deviation has occurred includes determining whether at least a portion of the additional sensor data for the second time period is outside of a threshold, wherein the threshold is based on the statistical parameters.

11. The method of claim 1, further comprising:

transmitting the alert including a link to a web interface, the web interface including the sensor data of the second time period in the context of the sensor data of the first time period.

12. The method of claim 11, further comprising:

receiving a feedback response to the alert, feedback response including feedback regarding clinical relevance of the alert;

taking action based on receipt of the feedback response.

13. The method of claim 12, wherein taking the action comprises:

adjusting the threshold, the sensor parameter used for the alert, or both based on the receipt of the feedback response.

14. The method of claim 1, wherein the activity pattern includes a plurality of features.

15. A method comprising:

accessing health data of a person for a first time period;

accessing sensor data from a plurality of motion sensors and a bed sensor deployed in a living unit for the first time period, the person residing in the living unit;

correlating the health data to at least a portion of the sensor data for the first time period;

accessing additional sensor data from the plurality of motion sensors and the bed sensor deployed in the living unit for a second time period, the second time period occurring after the first time period; and

determining whether a change in a health condition of the person has occurred based on the additional sensor data and correlation of the health data to at least the portion of the sensor data for the first time period.

16. The method of claim 15, further comprising:

generating an alert when a determination is made that the change in the health condition has occurred.

17. The method of claim 15, wherein the health condition is pulse pressure, pulse pressure being the difference between systolic blood pressure (SBP) and the diastolic blood pressure (DBP).

18. A method comprising:

accessing sensor data from a plurality of motion sensors and a bed sensor deployed in a living unit for a time period; and

generating a display based on access of the sensor data associated with the time period.

19. The method of claim 18, wherein the sensor data is grouped on the display based on a plurality of categories, the plurality of categories include motion, pulse, breathing, and restlessness.

20. The method of claim 18, further comprising:

receiving a selection of a person and a date range,

wherein accessing sensor data from the plurality of motion sensors and the bed sensor for the time period is based on receipt of the selection.

21. The method of claim 18, further comprising:

receiving a time interval modification request,

wherein generation of the display is based on access of the sensor data associated with the time period and receipt of the time interval modification request.

22. The method of claim 18, further comprising:

receiving a time increment modification request,

wherein generation of the display is based on access of the sensor data associated with the time period and receipt of the time increment modification request.

23. The method of claim 18, further comprising:

determining an away-from-home time period for a person associated with the living unit during the time period,

wherein generation of the display is based on access of the sensor data and a determination of the away-from-home time period.

24. The method of claim 23, wherein determining the away-from home time period comprises:

analyzing the sensor data to determine whether a living unit departure sensor sequence and a living unit return sensor sequence has occurred; and

calculating a time difference between occurrence of the living unit departure sensor sequence and occurrence of the living unit return sensor sequence, wherein the away-from home time period is based on the time difference.

25. The method of claim 24, wherein analyzing the sensor data comprises:

applying fuzzy logic to at least a portion of the sensor data to determine whether a living unit departure sensor sequence and a living unit return sensor sequence has occurred.

26. The method of claim 23, further comprising:

computing a number of motion sensor hits for a plurality of hours, a single motion sensor hit being associated with a particular motion sensor of the plurality of motion sensors, the time period including the plurality of hours; and

calculating density for the plurality of hours, the density for a particular hour of the plurality of hours being based on the number of motion sensor hits during the particular hour and the determination of the away-from-home time period,

wherein generation of the display is based on calculation of the density.

27. The method of claim 26, wherein generation of the display comprises:

selecting a plurality of color mappings, a particular color mapping having a color based on the density and being associated with a position based on the particular hour and a particular day, the time period including a plurality of days,

wherein generation of the display is based on selection of the plurality of color mappings.

28. A method comprising:

accessing a first density map and a second density map, the first density map having a plurality of first color mappings, the second density map having a plurality of second color mappings, a particular first color mapping having a color based on density and being associated with a position based on a particular hour and a particular day, the

density being based on a number of motion sensor hits during the particular hour and a determination of the away-from-home time period;

computing a dis-similarity between the first density map and the second density map based on a textual feature of the first density map and the second density map; and

generating a computational result based on computing the dis-similarity.

29. The method of claim 28, wherein the textual features include spatial, frequency, and perceptual properties.

30. A method comprising:

generating a plurality of feature clusters for a time period, the time period including a plurality of days, a particular feature cluster associated with a plurality of feature vectors, a particular feature vector associated sensor data from at least some of a plurality of motion sensors and a bed sensor deployed in a living unit;

accessing an additional sensor data associated a particular feature for a different time period;

determining whether the additional sensor data falls within the plurality of feature clusters or belongs in a new cluster; and

generating a notification based on a result of a determination.

31. The method of claim 30, wherein generating the notification comprises:

generating a cluster addition notification based on a determination that the additional sensor data falls within the plurality of feature clusters.

32. The method of claim 30, wherein generating the notification comprises:

generating a new cluster notification based on a determination that the additional sensor data belongs in the new cluster.

33. A non-transitory machine-readable medium comprising instructions, which when executed by one or more processors, cause the one or more processors to perform the following operations:

access sensor data from a plurality of motion sensors and a bed sensor deployed in a living unit for a first time period;

identify an activity pattern for the first time period based on at least a portion of sensor data associated with the first time period, the activity pattern representing a physical and cognitive health condition of a person residing in the living unit;

access additional sensor data from the plurality of motion sensors and the bed sensor deployed in the living unit for a second time period, the second time period occurring after the first time period;

determine whether a deviation of the activity pattern of the first time period has occurred for the second time period; and

generate an alert based on a determination that the derivation has occurred.

34. A system comprising:

a processor and a memory coupled to the processor;

a sensor data module deployed in the memory and executed by the processor to access sensor data from a plurality of motion sensors and a bed sensor deployed in a living unit for a first time period and to access additional sensor data from the plurality of motion sensors and the bed sensor deployed in the living unit for a second time period, the second time period occurring after the first time period;

an activity pattern identification module deployed in the memory and executed by the processor to identify an activity pattern for the first time period based on at least a portion of sensor data associated with the first time period, the activity pattern representing a physical and cognitive health condition of a person residing in the living unit;

a deviation module deployed in the memory and executed by the processor to determine whether a deviation of the activity pattern of the first time period identified by the activity identification module has occurred for the second time period; and

a display generation module deployed in the memory and executed by the processor to generate an alert based on a determination by the deviation module that the derivation has occurred.