METHOD FOR DETECTING AND RESPONDING TO FALLS BY RESIDENTS WITHIN A FACILITY

• US 20180000385A1
• Filed: 06/19/2017
• Published: 01/04/2018
• Est. Priority Date: 06/17/2016
• Status: Active Grant

• Current Assignee: Philips North America LLC (Koninklijke Philips N.V.)
• Original Assignee: Blue Willow Systems, Inc. (Koninklijke Philips N.V.)
• Inventors: Heaton, Richard, Devdas, Vikram, Erichsen, Dan, Edwards, Joe
• Granted Patent: US 10,226,204 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1117 Fall detection
• 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: G01P 15/00 Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
• CPC Class Codes: G01P 15/0891 with indication of predetermined acceleration values G01P15/135 takes precedence
• CPC Class Codes: G08B 21/043 detecting an emergency event, e.g. a fall
• CPC Class Codes: G08B 21/0446 worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
• CPC Class Codes: G08B 25/001 Alarm cancelling procedures or alarm forwarding decisions, e.g. based on absence of alarm confirmation
• CPC Class Codes: G08B 25/016 Personal emergency signalling and security systems emergency non-personal manually actuated alarm activators G08B25/12
• CPC Class Codes: G08B 29/186 Fuzzy logic; neural networks
• 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
• CPC Class Codes: H04M 3/5116 for emergency applications
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds

Abstract

One variation of a method for detecting and responding to falls by residents within a facility includes: at a wearable device worn by a resident, writing sensor data from a sensor integrated into the wearable device to a buffer, inputting sensor data into a compressed fall detection model—defining a compressed form of a complete fall detection model and stored locally on the wearable device—to detect a fall event at a first time, and transmitting a corpus of sensor data from the buffer and a cue for confirmation of the fall event to a local wireless hub in response to detecting the fall event; and, remotely from the wearable device, inputting the corpus of sensor data into the complete fall detection model—stored remotely from the wearable device—to confirm the fall event and dispatching a care provider to assist the resident in response to confirming the fall event.

Claims

1. A method for detecting and responding to falls by residents within a facility, the method comprising:

at a wearable device worn by a resident;

writing sensor data from a sensor integrated into the wearable device to a buffer;

inputting sensor data read from the sensor into a compressed fall detection model stored locally on the wearable device to detect a fall event at a first time, the compressed fall detection model defining a compressed form of a complete fall detection model;

in response to detecting the fall event at a first time;

wirelessly connecting to a local wireless hub;

transmitting a corpus of sensor data from the buffer and a cue for confirmation of the fall event to the local wireless hub, the corpus of sensor data corresponding to a duration of time terminating at approximately the first time; and

wirelessly disconnecting from the local wireless hub;

remotely from the wearable device, inputting the corpus of sensor data into the complete fall detection model, stored remotely from the wearable device, to confirm the fall event; and

in response to confirming the fall event, dispatching a care provider to assist the resident.

2. The method of claim 1:

wherein inputting sensor data into the compressed fall detection model to detect the fall event comprises;

passing sensor data read from the sensor into the compressed fall detection model comprising a first recurrent neural network defining a first quantity of nodes to calculate a first confidence score for occurrence of the fall event at the wearable device; and

detecting the fall event in response to the first confidence score exceeding a local threshold score;

wherein inputting the corpus of sensor data into the complete fall detection model to confirm the fall event comprises;

passing the corpus of sensor data into the complete fall detection model comprising a second recurrent neural network to calculate a second confidence score for occurrence of the fall event at the wearable device, the second recurrent neural network defining a second quantity of nodes greater than the first quantity of nodes; and

confirming the fall event in response to the second confidence score exceeding a global threshold score greater than the local threshold score.

3. The method of claim 2, further comprising, at the wearable device:

tracking a step rate of the resident based on motion data read from an inertial sensor integrated into the wearable device;

tracking a skin temperature of the resident based on temperature values read from a temperature sensor integrated into the wearable device; and

setting the local threshold score as an inverse function of step rate and deviation of skin temperature of the resident from a target skin temperature.

4. The method of claim 2, further comprising:

calculating a frequency of historical fall events, stored in a fall event database, across a group of residents as a function of time of day; and

setting the global threshold score as an inverse function of frequency of historical fall events at a current time of day.

5. The method of claim 1:

further comprising;

at the wearable device, broadcasting a beacon signal;

calculating a location of the resident based on a known location of the local wireless hub within the facility in response to receipt of the beacon signal from the wearable device at the local wireless hub;

wherein dispatching the care provider to assist the resident comprises;

generating a notification comprising a prompt to assist the resident;

inserting a pointer to the location into the notification; and

transmitting the notification to a computing device affiliated with the care provider.

6. The method of claim 5:

wherein writing sensor data to the buffer comprises writing motion data read from an accelerometer and a gyroscope, integrated into the wearable device, to the buffer defining a duration of multiple minutes; and

wherein broadcasting the beacon signal comprises broadcasting the beacon signal multiple times per minute.

7. The method of claim 5, further comprising:

at the wearable device, outputting an audible alarm at a second time in response to detecting the fall event;

at the remote computer system;

verifying the fall event at the wearable device in response to failure of the resident to clear the alarm within a threshold duration following the second time at the wearable device; and

deescalating a priority of the notification at the computing device in response to receipt of an input to clear the alarm within the threshold duration following the second time at the wearable device.

8. The method of claim 1:

further comprising, at the local wireless hub;

attempting connection to a remote computer system via a computer network;

in response to establishing connection to the remote computer system, transmitting the corpus of sensor data to the remote computer system; and

in response to failing to establish connection to the remote computer system;

inputting the corpus of sensor data into an intermediate fall detection model to confirm the fall event, the intermediate fall detection model stored on the local wireless hub, defining a second abbreviated form of the complete fall detection model, and exhibiting an intermediate memory footprint greater than a compressed memory footprint of the compressed fall detection model; and

in response to confirming the fall event, transmitting a local prompt to assist the resident, through a local wireless network, to a computing device affiliated with the care provider;

wherein inputting the corpus of sensor data into the complete fall detection model to confirm the fall event comprises, at a remote computer system, inputting the corpus of sensor data into the complete fall detection model to confirm the fall event in response to receiving the corpus of sensor data from the local wireless hub via the computer network; and

wherein dispatching the care provider comprises transmitting a global prompt to assist the resident, through the computer network, to the computing device affiliated with the care provider.

9. The method of claim 1, further comprising:

at a remote computer system;

generating the complete fall detection model characterized by a first memory footprint;

generating an intermediate fall detection model characterized by a second memory footprint less than the first memory footprint and representing an abbreviated form of the complete fall detection model;

generating the compressed fall detection model characterized by a third memory footprint less than the second memory footprint;

distributing the intermediate fall detection model to the local wireless hub via a computer network; and

distributing the compressed fall detection model to the wearable device via the computer network.

at the local wireless hub;

in response to receiving the corpus of sensor data from the wearable device, inputting the corpus of sensor data into an intermediate fall detection model to detect the fall event; and

in response to detecting the fall event, returning the corpus of sensor data to the remote computer system executing the complete fall detection model to confirm the fall event.

10. The method of claim 1:

wherein inputting sensor data into the compressed fall detection model to detect the fall event comprises;

calculating a confidence score for occurrence of the fall event at the wearable device based on;

a low-frequency change in ambient pressure values, read from an ambient pressure sensor integrated into the wearable device over the duration of time and

similarity of motion data, read from an accelerometer and a gyroscope integrated into the wearable device over the duration of time, to a motion pattern of a fall represented in the compressed fall detection model; and

wherein writing sensor data to the buffer comprises writing ambient pressure values read from the ambient pressure sensor and motion data read from the accelerometer and the gyroscope to the buffer; and

wherein transmitting the corpus of sensor data to the local wireless hub comprises transmitting ambient pressure values and motion data to the local wireless hub in response to the confidence score exceeding a threshold score.

11. The method of claim 1:

wherein writing sensor data into the wearable device to the buffer comprises writing sensor data to the buffer stored locally on the wearable device worn on a wrist of the resident;

further comprising, in response detecting the fall event at the wearable device, transmitting a prompt to a second wearable device arranged on the resident to transmit sensor data contained in a second buffer stored on the second wearable device to the local wireless hub; and

wherein inputting the corpus of sensor data into the complete fall detection model remotely from the wearable device to confirm the fall event comprises confirming the fall event remotely from the wearable device and the second wearable device based on the corpus of sensor data received from the wearable device and sensor data received from the second wearable device.

12. The method of claim 1:

wherein dispatching the care provider to assist the resident comprises;

accessing a digital incident report;

inserting a name of the resident, affiliated with the wearable device, into the digital incident report;

inserting a prompt to verify the fall event into the digital incident report; and

transmitting the digital incident report to a computing device affiliated with the care provider; and

further comprising;

extracting verification of the fall event from the digital incident report completed by the care provider at the computing device;

labeling the corpus of sensor data with verified fall event labels;

appending a training set with the corpus of sensor data; and

retraining the complete fall detection model on the training set.

13. The method of claim 12, further comprising, in response to retraining the complete fall detection model:

retraining the compressed fall detection model on the training set, the compressed fall detection model exhibiting a compressed memory footprint smaller than a complete memory footprint of the complete fall detection model; and

pushing an update for the compressed fall detection model to the wearable device via a computer network.

14. The method of claim 1, further comprising:

at the wearable device, transmitting an alert and a second corpus of sensor data from the buffer to the local wireless hub in response to receiving selection of an input region on the wearable device at a second time, the second corpus of sensor data different from the first corpus of sensor data and comprising sensor data recorded to the buffer by the wearable device up to the second time;

remotely from the wearable device;

in response to receiving the alert, dispatching a second care provider to assist the resident;

accessing confirmation of a second fall event, involving the resident, submitted by the second care provider;

scanning the second corpus of sensor data for a segment of sensor data exhibiting correlation to a fall;

labeling the segment of sensor data with a fall event label;

appending a training set with the segment of sensor data; and

retraining the complete fall detection model according to the training set.

15. The method of claim 1:

wherein writing sensor data to the buffer comprises writing sensor data to the buffer defining a first duration;

further comprising;

at the wearable device, writing a second corpus of sensor data to a second buffer defining a second duration greater than the first duration;

remotely from the wearable device;

at a second time, receiving verification of a second fall event from a second care provider, the second fall event involving the resident and undetected by the wearable device;

querying the wearable device for the second buffer;

scanning the second corpus of sensor data for a segment of sensor data exhibiting correlation to a fall;

labeling the segment of sensor data with a fall event label;

appending a training set with the segment of sensor data; and

retraining the complete fall detection model according to the training set.

16. A system for detecting and responding to falls by residents within a facility, the system comprising:

a set of local wireless hubs distributed throughout the facility;

a wearable device assigned to a resident occupying the facility, comprising a motion sensor and an ambient pressure sensor, and configured to;

write motion data read from the motion sensor and ambient pressure data read from the ambient pressure sensor to a buffer;

pass motion data and ambient pressure data into a compressed fall detection model stored locally to detect a fall event at a first time, the compressed fall detection model defining a compressed form of a complete fall detection model; and

broadcast a corpus of sensor data from the buffer and a cue for confirmation of the fall event to a first local wireless hub in response to detecting the fall event, the first local wireless hub in the set of local wireless hubs and proximal the wearable device at the first time, the corpus of sensor data corresponding to a duration of time terminating at approximately the first time; and

a remote computer system remote from the facility and configured to;

receive the corpus of sensor data from the first local wireless hub via a computer network;

pass the corpus of sensor data into the complete fall detection model to confirm the fall event; and

transmit a prompt to assist the resident to a computing device affiliated with a care provider within the facility in response to confirming the fall event.

17. The system of claim 16, wherein the wearable device is configured to transiently couple to a wrist of the resident and comprises a wireless communication module configured to:

regularly broadcast a beacon signal at a first rate;

regularly broadcast contents of the buffer to a local wireless hub, in the set of local wireless hubs, nearby at a second rate less than the first rate; and

broadcast the corpus of sensor data from the buffer to the first local wireless hub in real-time in response to detecting the fall event.

18. The system of claim 16, wherein the first local wireless hub is configured to:

attempt connection to the remote computer system via the computer network in response to receiving the corpus of sensor data from the wearable device;

transmit the corpus of sensor data to the remote computer system via the computer network in response to establishing connection to the remote computer system; and

in response to failing to establish connection to the remote computer system;

pass the corpus of sensor data into an intermediate fall detection model to confirm the fall event, the intermediate fall detection model stored on the local wireless hub, defining a second abbreviated form of the complete fall detection model, and exhibiting an intermediate memory footprint greater than a compressed memory footprint of the compressed fall detection model; and

in response to confirming the fall event, transmit a local prompt to assist the resident, through a local wireless network, to a set of computing devices affiliated with care providers occupying the facility.

19. The method of claim 16:

wherein the wearable device;

defines an input region; and

is configured to transmit an alert and a second corpus of sensor data from the buffer to a second wireless hub in response to receiving selection of the input region at a second time, the second local wireless hub in the set of local wireless hubs and adjacent the wearable device at the second time, the second corpus of sensor data different from the first corpus of sensor data and comprising sensor data recorded to the buffer up to the second time;

wherein the remote computer system is further configured to;

dispatch a second care provider to assist the resident in response to receiving the alert;

access confirmation of a second fall event, involving the resident, submitted by the second care provider;

scan the second corpus of sensor data for a segment of sensor data exhibiting correlation to a fall;

label the segment of sensor data with a fall event label;

append a training set with the segment of sensor data; and

retrain the complete fall detection model according to the training set.

20. The method of claim 16:

further comprising an application;

executing on the computer system affiliated with the care provider;

configured to serve a digital incident report to the care provider; and

return the digital accident response to the remote computer system;

wherein the remote computer system is further configured to;

extract verification of the fall event from the digital incident report completed by the care provider via the application;

label the corpus of sensor data with verified fall event labels;

append a training set with the corpus of sensor data;

retrain the complete fall detection model on the training set;

retrain the compressed fall detection model on the training set, the compressed fall detection model exhibiting a compressed memory footprint smaller than a complete memory footprint of the complete fall detection model; and

push an update for the compressed fall detection model to the wearable device via the computer network.