Activities of Daily Living Monitoring and Reporting System

• US 20180325426A1
• Filed: 02/21/2018
• Published: 11/15/2018
• Est. Priority Date: 05/12/2017
• Status: Active Application

• Current Assignee: Algorithmic Intuition, Inc.
• Original Assignee: Algorithmic Intuition, Inc.
• Inventors: Shaw, Kevin A., Brown, Dan W.
• Application Number:
  US15/901,764
• Publication Number:
  US 20180325426A1
• Time in Patent Office: Days
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/6823 Trunk, e.g., chest, back, abdomen, hip
• CPC Class Codes: A61B 5/6833 Adhesive patches
• CPC Class Codes: G01C 19/00 Gyroscopes; Turn-sensitive devices using vibrating masses; Turn-sensitive devices without moving masses; Measuring angular rate using gyroscopic effects
• CPC Class Codes: G01P 13/00 Indicating or recording presence, absence, or direction, of movement electric switches H01H; counting moving objects G06M7/00
• CPC Class Codes: G01P 15/00 Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
• CPC Class Codes: G01P 15/18 in two or more dimensions
• CPC Class Codes: G06F 11/3013 where the computing system is an embedded system, i.e. a combination of hardware and software dedicated to perform a certain function in mobile devices, printers, automotive or aircraft systems testing or monitoring of control systems or parts thereof G05B23/02
• CPC Class Codes: G06F 11/3438 monitoring of user actions tracking the activity of the user H04L67/535
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A user-wearable electronic device includes a housing configured to be worn on a user'"'"'s torso, a plurality of sensors disposed in the housing, including a first sensor to sense motion of the user and produce raw activities of daily living (ADL) data, and a biometric sensor to sense one or more biometric characteristics of the user. One or more processors in the electronic device or an intermediary device generate, for a sequence of time periods, ADL identification information by processing the raw ADL data using one or more neural networks pre-trained to recognize a predefined set of ADLs. Each pre-trained neural network includes a plurality of neural network layers, including at least one layer that includes a recurrent neural network. Reports that include ADL information corresponding to the generated ADL identification information for time periods in the sequence of time periods are transmitted to a monitoring system.

Claims

1. A user-wearable electronic device, for monitoring user activities of daily living (ADL), comprising:

a housing configured to be worn on a user'"'"'s torso;

a plurality of sensors disposed in the housing, including a first sensor to produce raw ADL data, and a biometric sensor configured to sense one or more biometric characteristics of the user and generate corresponding biometric data;

one or more processors, disposed in the housing and coupled to the one or more sensors, configured to;

for each time period in a sequence of successive time periods,

generate ADL identification information for the time period by processing the raw ADL data produced by the first sensor using one or more neural networks pre-trained to recognize a predefined set of ADLs, the pre-trained one or more neural networks each including a plurality of neural network layers, at least one layer of the plurality of neural network layers comprising a recurrent neural network, wherein an output of the one or more neural networks for each time period corresponds to the generated ADL identification information for the time period; and

a transmitter, disposed in the housing and coupled to at least one of the one or more processors, to transmit, at predefined times, reports for the user, wherein a respective report for the user includes ADL information corresponding to the generated ADL identification information for one or more time periods in the sequence of time periods.

2. The user-wearable device of claim 1, wherein the biometric sensor and housing are configured to operatively couple the biometric sensor to the user'"'"'s skin.

3. The user-wearable device of claim 1, wherein the housing includes an interface configured to be in direct contact with the user'"'"'s skin, and the biometric sensor is coupled to the interface.

4. The user-wearable device of claim 1, wherein the respective report for the user further includes biometric information corresponding to the biometric data generated by the biometric sensor for a time period corresponding to the respective report.

5. The user-wearable device of claim 1, wherein the biometric sensor includes at least one of:

a temperature sensor for sensing temperature of the user, an EKG sensor for detecting electrical activity associated with the user'"'"'s heart, a heart rate sensor for measuring the user'"'"'s heart rate, and a blood pressure sensor for measuring at least one parameter of the user'"'"'s blood pressure.

6. The user-wearable device of claim 1, wherein the transmitter is configured to transmit the reports to a remotely located monitoring system that monitors ADL information for a plurality of users.

7. The user-wearable device of claim 1, wherein the predefined set of ADLs includes three or more activities selected from the group consisting of dressing, eating, transferring/ambulation, continence/toileting, bathing/hygiene, sitting, and sleeping/napping.

8. The user-wearable device of claim 7, wherein the predefined set of ADLs further includes one or more activities selected from the group consisting of shopping, housekeeping, meal preparation, transportation, and talking/socializing/communication.

9. The user-wearable device of claim 1, wherein the first sensor comprises an accelerometer, an orientation sensor, motion sensor, or gyroscopic sensor.

10. The user-wearable device of claim 1, further comprising a location or proximity sensor disposed in or on the housing;

wherein the one or more processors are further configured to;

determine location information for the user based on data from the location or proximity sensor; and

generate at least a portion of the ADL identification information for the time period by processing the raw ADL data produced by the first sensor and the location information for the user using at least one of the one or more neural networks.

11. The user-wearable device of claim 10, wherein the location or proximity sensor is configured to obtain or generate range or proximity information corresponding to a range or proximity to one or more beacons at known locations in an environment occupied by the user;

and wherein at least one processor of the one or more processors is configured to determine the location information for the user based on the range or proximity information.

12. The user-wearable device of claim 1, wherein the transmitter is configured to wirelessly transmit the reports for the user to an intermediary device co-located with the user-wearable device that forwards the reports for the user to a target system.

13. The user-wearable device of claim 1, wherein the one or more processors is configured to generate the ADL identification information for a respective time period in the sequence of time periods by:

generating a set of scores, including one or more scores for each ADL in the predefined set of ADLs;

in accordance with the generated set of scores, determining a dominant activity for the respective time period, wherein the dominant activity is one of the ADLs in the predefined set of ADLs;

in accordance with a determination that the one or more scores for the dominant activity for the respective time period meets predefined criteria, including in the generated ADL identification information for the respective time period information identifying the dominant activity for the respective time period.

14. The user-wearable device of claim 13, wherein the one or more processors are further configured to generate the ADL identification information for a respective time period in the sequence of time periods by:

in accordance with a determination that the one or more scores for the dominant activity for the respective time period do not meet the predefined criteria, including in the generated ADL identification information for the respective time period information indicating that the user'"'"'s activity during the respective time period has not been classified as any of the ADLs in the predefined set of ADLs.

15. The user-wearable device of claim 1, wherein the predefined set of ADLs includes N distinct ADLs, where N is an integer greater than 2, and the ADL identification information generated by the one or more processors for the time period includes a vector of having at least N+1 elements, only one of which is set to a non-null value.

16. The user-wearable device of claim 1, wherein the predefined set of ADLs includes N distinct ADLs, where N is an integer greater than 2, and the ADL identification information generated by the one or more processors for the time period includes a vector of having at least N elements, only one of which is set to a non-null value.

17. The user-wearable device of claim 1, wherein

the pre-trained one or more neural networks include a first neural network having a first configuration;

the user-wearable device includes a receiver, disposed in the housing and coupled to at least one processor of the one or more processors, to receive an updated configuration for the first neural network; and

the one or more processors are further configured to reconfigure the first neural network with the updated configuration, and to thereafter generate ADL identification information for time periods subsequent to the reconfiguring of the first neural network, using the first neural network configured using the updated configuration.

18. The user-wearable device of claim 17, wherein the transmitter and the receiver comprise a wireless transceiver.

19. The user-wearable device of claim 1, wherein the housing has a length no greater than 10 cm, a width no greater than 6.5 cm and a thickness no greater than 0.5 cm.

20. The user-wearable device of claim 1, wherein the housing and all components within the housing have a total weight no greater than 60 grams.

21. The user-wearable device of claim 1, wherein the successive time periods each have a duration of no more than 30 seconds, and the predefined times at which the transmitter transmits reports for the user occur at intervals of no less than 5 minutes.

22. The user-wearable device of claim 1, wherein the one or more processors are configured to receive raw ADL data from the first sensor at a rate of no less than 10 samples per second, in accordance with a sampling period, and a ratio of the time period to the sampling period is no less than 100.

23. The user-wearable device of claim 1, wherein the one or more processors are configured to store the raw ADL data for one or more of the time periods in the sequence of time period, and to initiate transmission of the stored raw ADL data to the target system using the transmitter.

24. The user-wearable device of claim 1, wherein the one or more processors are further configured to:

automatically detect an emergency, based on the raw ADL data and/or biometric data, in accordance with predefined emergency detection criteria, and in response to the automatic detection of the emergency, to initiate transmission of an emergency report to the target system using the transmitter.

25. The user-wearable device of claim 1, further comprising a rechargeable battery disposed within the housing, wherein the one or more processors are further configured to:

perform a predefined set of tasks while the user-wearable device is determined to be connected to a power source for recharging the user-wearable device'"'"'s battery, the predefined set of tasks including transmitting recorded information not transmitted when the user-wearable device is connected to a power source for recharging the user-wearable device'"'"'s battery, and receiving update information for reconfiguring at least one aspect of the user-wearable device.

26. A server system, comprising:

memory storing a plurality of neural network configurations, each neural network configurations corresponding to a distinct classification or a distinct group of classifications;

one or more processors for executing one or more programs; and

a communication interface, coupled to at least one of the one or more processors to provide each of the neural network configurations to corresponding user-wearable electronic devices or intermediary devices, each user-wearable electronic device or intermediary device for identifying user activities of daily living (ADL) of a respective user assigned to a classification or group of classifications corresponding to the neural network configuration provided to the user-wearable electronic device or intermediary device.

27. The server system of claim 26, wherein a respective user-wearable electronic device of the corresponding user-wearable electronic devices or a respective intermediary device of the intermediary devices includes:

one or more neural networks configured with the neural network configuration provided by the server system to generate, for each time period in a sequence of successive time periods, ADL identification information for the time period by processing raw ADL data produced by a first sensor using one or more neural networks pre-trained to recognize a predefined set of ADLs, the one or more neural networks each including a plurality of neural network layers, at least one layer of the plurality of neural network layers comprising a recurrent neural network, wherein an output of the one or more neural networks for each time period corresponds to the generated ADL identification information for the time period.

28. An activities of daily living (ADL) monitoring system, comprising:

one or more processors, configured to;

collect raw ADL data produced by a first sensor;

for each time period in a sequence of successive time periods, generate ADL identification information for the time period by processing the raw ADL data produced by the first sensor using one or more neural networks pre-trained to recognize a predefined set of ADLs, the pre-trained one or more neural networks each including a plurality of neural network layers, at least one layer of the plurality of neural network layers comprising a recurrent neural network, wherein an output of the one or more neural networks for each time period corresponds to the generated ADL identification information for the time period; and

transmit one or more reports corresponding to the user, wherein a respective report corresponding to the user includes ADL information corresponding to the generated ADL identification information for one or more time periods in the sequence of time periods.

29. The ADL monitoring system of claim 28, wherein

the system includes a user-wearable electronic device and an intermediary device configured to receive raw ADL from the user-wearable electronic device;

the first sensor is disposed in a housing, located in the user-wearable electronic device, the housing configured to be worn by or affixed to a device worn by a user; and

the ADL identification information is generated by one or more processors in the intermediary device using the one or more neural networks pre-trained to recognize a predefined set of ADLs.

30. The ADL monitoring system of claim 28, wherein the predefined set of ADLs includes three or more activities selected from the group consisting of dressing, eating, transferring/ambulation, continence/toileting, bathing/hygiene, sitting, and sleeping/napping.