System and method for monitoring activities of daily living of a person

• US 10,147,298 B2
• Filed: 01/07/2016
• Issued: 12/04/2018
• Est. Priority Date: 01/12/2015
• Status: Active Grant

• Current Assignee: Koninklijke Philips N.V.
• Original Assignee: Koninklijke Philips N.V.
• Inventors: Ten Kate, Warner Rudolph Theophile
• Primary Examiner(s): Wang, Jack K
• Application Number:
  US15/542,257
• Publication Number:
  US 20170365149A1
• Time in Patent Office: 1,062 Days
• Field of Search: 3405731
• CPC Class Codes: A61B 2503/08 Elderly
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 2560/0252 using ambient temperature
• 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 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• 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 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G08B 21/0423 detecting deviation from an expected pattern of behaviour or schedule
• CPC Class Codes: G08B 21/0453 worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing detecting, measuring or recording for diagnostic purposes A61B5/00
• CPC Class Codes: G08B 21/0492 Sensor dual technology, i.e. two or more technologies collaborate to extract unsafe condition, e.g. video tracking and RFID tracking
• CPC Class Codes: G08B 29/185 Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
• CPC Class Codes: G08B 29/188 Data fusion; cooperative systems, e.g. voting among different detectors
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Presented is a system and method for monitoring activities of daily living, ADLs, of a person within an environment. The system comprises an ADL inference unit adapted to receive a sensor output signal representative of a detected value of a property of at least one of: the person; and the environment, and to generate an inferred ADL output signal representative of the inferred ADL of the person. A monitor unit is adapted to generate a monitor signal dependent on at least one of: the received sensor output signal; and the inferred ADL output signal.

Claims

1. A system for monitoring activities of daily living, ADLs, of a person within an environment, the system comprising:

an ADL inference unit adapted to receive a sensor output signal representative of a detected value of a property of at least one of;

the person; and

the environment, and to generate an inferred ADL output signal representative of an inferred ADL of the person; and

a monitor unit adapted to generate a monitor signal dependent on at least one of;

the received sensor output signal; and

the inferred ADL output signal,

wherein the ADL inference unit comprises;

a first inference subsystem adapted to receive the sensor output signal, to infer a primitive ADL event based on the received sensor output signal, the primitive ADL event being an event directly inferable from at least one sensor output signal, and to generate a primitive ADL signal representative of the inferred primitive ADL event; and

a second inference subsystem adapted to receive the primitive ADL signal, to infer an aggregate ADL event from the received primitive ADL signal, the aggregate ADL event being an event based on at least one primitive ADL event, and to generate an aggregate ADL signal representative of the inferred aggregate ADL event, and wherein ADL inference unit is adapted to generate the inferred ADL output signal based on the primitive ADL signal and the aggregate ADL signal.

2. The system according to claim 1, wherein the second inference subsystem is adapted to infer the aggregate ADL event from the received primitive ADL signal using a rule-based inference method.

3. The system according to claim 2, further comprising a rule input interface adapted to receive an input for defining or modifying one or more rules of the rule-based inference method.

4. The system according to claim 1, further comprising a user input interface adapted to receive a user input for defining or modifying one or more alert conditions, and wherein the monitor unit is adapted to generate the monitor signal further dependent on the one or more alert conditions.

5. The system according to claim 1, wherein the monitor unit is adapted to generate the monitor signal based on a comparison of the received sensor output signal with a predetermined threshold.

6. The system according to claim 1, wherein the inferred ADL of the person is taken from a group comprising one or more of:

eating, cooking, medicating, sleeping, toileting, bathing, or washing.

7. The system according to claim 1, wherein the ADL inference unit is further adapted to store the inferred ADL of the person in an activity database.

8. The system according to claim 7, wherein the monitor unit is further adapted to detect an irregularity in the activity database, and wherein the monitor unit is further arranged to generate the monitor signal as an alert signal in response to the detected irregularity.

9. The system according to claim 8, wherein the monitor unit is further adapted to determine a frequency of the ADL in the activity database, the detected irregularity being dependent on a change in the frequency.

10. The system according to claim 1, further comprising a sensor adapted to detect a value of a property of at least one of the person and the environment, and to generate the sensor output signal representative of the detected value.

11. The system according to claim 1, wherein the system is adapted to provide the generated monitor signal to at least one of the person, a medical practitioner, or a caregiver.

12. A method for monitoring activities of daily living, ADLs, of a person within an environment, the method comprising:

inferring an ADL of the person based on a sensor output signal representative of a detected value of a property of at least one of;

the person; and

the environment;

generating an inferred ADL output signal representative of the inferred ADL of the person; and

generating a monitor signal dependent on at least one of;

the sensor output signal; and

the inferred ADL output signal,

wherein the step of inferring an ADL comprises;

inferring a primitive ADL event based on the sensor output signal, the primitive ADL event being an event directly inferable from at least one sensor output signal;

generating a primitive ADL signal representative of the inferred primitive ADL event;

inferring an aggregate ADL event from the primitive ADL signal, the aggregate ADL event being an event based on at least one primitive ADL event; and

generating an aggregate ADL signal representative of the inferred aggregate ADL event, and wherein the step of generating an inferred ADL output signal ADL is based on the primitive ADL signal and the aggregate ADL signal.

13. The method of claim 12, further comprising storing the inferred ADL of the person in an activity database,

wherein generating a monitor signal comprises detecting an irregularity in the activity database and generating an alert signal in response to the detected irregularity.