System and method for the inference of activities of daily living and instrumental activities of daily living automatically

• US 9,968,283 B2
• Filed: 11/24/2014
• Issued: 05/15/2018
• Est. Priority Date: 03/10/2004
• Status: Active Grant

• Current Assignee: University of Virginia Patent Foundation (University of Virginia)
• Original Assignee: University of Virginia Patent Foundation (University of Virginia)
• Inventors: Alwan, Majd, Felder, Robin A., Kell, Steven W., Wood, Sarah G., Cvetanovich, Michael, Turner, Beverly L., Holman, J. William
• Primary Examiner(s): Eastwood, David C
• Assistant Examiner(s): JIAN, SHIRLEY XUEYING
• Application Number:
  US14/551,882
• Publication Number:
  US 20150080767A1
• Time in Patent Office: 1,268 Days
• Field of Search: 600300-301
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 2562/046 in a matrix array
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1113 Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/1115 Monitoring leaving of a patient support, e.g. a bed or a wheelchair
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/202 Assessing bladder functions, e.g. incontinence assessment
• CPC Class Codes: A61B 5/6887 mounted on external non-worn devices, e.g. non-medical devices
• CPC Class Codes: A61B 5/6889 Rooms
• 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

Abstract

A method and related system to, among other things, automatically infer answers to all of the ADL questions and the first four questions of the IADL in the home. The inference methods detect the relevant activities unobtrusively, continuously, accurately, objectively, quantifiably and without relying on the patient'"'"'s own memory (which may be fading due to aging or an existing health condition, such as Traumatic Brain Injury (TBI)) or on a caregiver'"'"'s subjective report. The methods rely on the judicious placement of a number of sensors in the subject'"'"'s place of residence, including motion detection sensors in every room, the decomposition of each relevant activity into the sub-tasks involved, identification of additional sensors required to detect the relevant sub-tasks and spatial-temporal conditions between the signals of sensors to formulate the rules that will detect the occurrence of the specific activities of interest.

Claims

1. An automated method of inferring an activity of daily living of a human subject, the method comprising:

date and time stamping sensor readings from a plurality of sensors;

storing a computer-readable log of the date and time stamped sensor readings; and

a computer processor obtaining the stored date and time stamped sensor readings and applying a set of rules that analyze spatial-temporal relationships of multiple sensors'"'"' readings to automatically infer that the human subject has engaged in a predetermined activity of interest,

wherein said spatial-temporal relationships are defined as sequences of said multiple sensors'"'"' readings and locations of said plurality of sensors, and

wherein the computer processor inferring that the subject has engaged in the activity of interest includes the computer processor making an inference as to whether the subject has engaged in the activity of interest without assistance from another person.

2. The method of claim 1, wherein the plurality of sensors are disposed in different rooms of a living area, and the inference that the subject has engaged in the activity of interest without assistance from another person includes application of a rule that (a) recognizes that the activity of interest is taking place in a first room of the living area, (b) infers that another person is present in the living area, and (c) infers that the subject has engaged in the activity of interest without assistance from the other person based on continued sensor activity outside the first room while the subject is engaging in the activity of interest.

3. The method of claim 1, wherein said rules include a rule that infers bathing/showering of the subject based on time and readings from at least a humidity sensor and a presence sensor.

4. The method of claim 3, wherein the rule that infers the bathing/showering of the subject discriminates between other events that would raise humidity in a room where the activity of interest is detected.

5. The method of claim 1, wherein said rules include a rule that infers the ability of the subject to prepare cooked meals based on time and readings from at least a temperature sensor and a presence sensor.

6. The method of claim 1, wherein said rules include a rule that infers the ability of the subject to use the toilet.

7. The method of claim 6, wherein the rule that infers the ability of the subject to use the toilet discriminates between toileting and other predetermined events that would take place in a room where the activity of interest is detected.

8. The method of claim 1, wherein said rules include a rule that infers the ability of the subject to perform a predetermined ambulatory activity without assistance.

9. The method of claim 8, wherein the rule that infers the ability of the subject to perform the predetermined ambulatory activity includes consideration of motion sensor readings showing continued activity during the activity period outside of a room where the activity of interest is detected.

10. The method of claim 1, wherein the inference as to whether the subject has engaged in the activity of interest without assistance from another person is based on a historical analysis of motion detected prior to the activity of interest in a plurality of locations.

11. A system for inferring an activity of daily living of a human subject, the system comprising:

a plurality of sensors disposed in different rooms of a living area; and

a microprocessor configured to;

receive multiple sensor readings from the plurality of sensors;

store a time stamped log of the sensor readings; and

apply a set of rules that analyze spatial-temporal relationships of multiple sensor readings to automatically infer that the human subject has engaged in a predetermined activity of interest,

wherein said spatial-temporal relationships are defined as sequences of said multiple sensor readings and locations of said plurality of sensors, and

wherein the microprocessor is further configured to infer whether the subject has engaged in the activity of interest without assistance from another person based on a historical analysis of motion detected prior to the activity of interest in a plurality of locations.

12. The system of claim 11, wherein the inference that the subject has engaged in the activity of interest without assistance from another person includes application of a rule that (a) recognizes that the activity of interest is taking place in a first room of the living area, (b) infers that another person is present in the living area, and (c) infers that the subject has engaged in the activity of interest without assistance from the other person based on continued sensor activity outside the first room while the subject is engaging in the activity of interest.

13. The system of claim 11, wherein said rules include a rule that infers bathing/showering of the subject based on time and readings from at least a humidity sensor and a presence sensor.

14. The system of claim 13, wherein the rule that infers the bathing/showering of the subject discriminates between other events that would raise humidity in a room where the activity of interest is detected.

15. The system of claim 11, wherein said rules include a rule that infers the ability of the subject to prepare cooked meals based on time and readings from at least a temperature sensor and a presence sensor.

16. The system of claim 11, wherein said rules include a rule that infers the ability of the subject to use the toilet and that discriminates between toileting and other predetermined events that would take place in a room where the activity of interest is detected.

17. The system of claim 11, wherein said rules include a rule that infers the ability of the subject to perform a predetermined ambulatory activity without assistance.

18. The system of claim 17, wherein the rule that infers the ability of the subject to perform the predetermined ambulatory activity includes consideration of motion sensor readings showing continued activity during the activity period outside of a room where the activity of interest is detected.

19. A computer program product comprising non-transitory computer readable storage medium having computer program logic that causes at least one processor in a computer system to perform operations including:

date and time stamping sensor readings from a plurality of sensors;

storing a log of the date and time stamped sensor readings; and

applying a set of rules by a computer processor that analyze spatial-temporal relationships of multiple sensors'"'"' readings to automatically infer that the human subject has engaged in a predetermined activity of interest,

wherein said spatial-temporal relationships are defined as sequences of said multiple sensors'"'"' readings and locations of said multiple sensors, and

wherein the inference by the computer processor that the subject has engaged in the activity of interest includes an inference by the computer processor as to whether the subject has engaged in the activity of interest without assistance from another person based on a historical analysis of motion detected prior to the activity of interest in a plurality of locations.

20. The computer program product of claim 19, wherein said rules include rules that infer at least three of:

bathing/showering of the subject based on time and readings from at least a humidity sensor and a presence sensor;

the ability of the subject to prepare cooked meals based on time and readings from at least a temperature sensor and a presence sensor;

the ability of the subject to use the toilet; and

/or

the ability of the subject to perform a predetermined ambulatory activity without assistance.