SYSTEM AND METHOD FOR REMOTE MONITORING FOR ELDERLY FALL PREDICTION, DETECTION, AND PREVENTION

1 . A method comprising:

collecting sensor data at a biomechanical sensing device coupled to a user, wherein the sensor data includes at least accelerometer data;

performing biomechanical analysis on the sensor data and thereby generating mobility metrics of the user, wherein performing biomechanical analysis comprises:

quantifying a set of gait dynamics as a component of the mobility metrics;

processing the mobility metrics in a risk assessment model and thereby generating a fall risk assessment; and

detecting a trigger condition and triggering a response to the fall risk assessment.

2 . The method of claim 1 , wherein performing biomechanical analysis further comprises generating a user activity graph as a component of the mobility metrics, wherein the activity graph characterizes activity states over a time period.

3 . The method of claim 1 , wherein triggering a response to the fall risk assessment comprises prompting the user to rest through a feedback interface.

4 . The method of claim 3 , wherein generating the fall risk assessment comprises generating a rest prediction metric; and wherein prompting the user to rest further comprises prompting the user to rest for an amount of time specified by the rest prediction metric.

5 . The method of claim 1 , wherein detecting the trigger condition and triggering the response comprises detecting elevated risk indicated through the fall risk assessment and transmitting a communication to a caretaker.

6 . The method of claim 1 , wherein quantifying a set of gait dynamics comprises generating a stride shuffle metric, double-stance metric, and a tremor metric; and wherein processing the mobility metrics in a risk assessment model comprises increasing the risk of a fall in the fall risk assessment with detected increases in shuffle-associated strides, double-stance-associated strides, or the amount of tremors.

7 . The method of claim 1 , wherein triggering a response comprises reporting incidents from the set of: fall events, stumble events, tremor time, and double stance time.

8 . The method of claim 1 , further comprising measuring the quality of user mobility as reflected in the mobility metrics over an extended duration and generating a rehabilitation progress report.

9 . The method of claim 1 , wherein the set of gait dynamics includes gait shuffling classification; and wherein quantifying a set of gait dynamics comprises detecting vertical step displacements, classifying the gait as shuffling when vertical step displacements satisfy a shuffle condition, and thereby classifying segments of sensor data as gait shuffling.

10 . The method of claim 1 , wherein the set of gait dynamics includes a gait asymmetry metric; and wherein quantifying a set of gait dynamics comprises detecting right step and left step lengths, comparing the right step length and left step length, and thereby generating a gait asymmetry metric.

11 . The method of claim 1 , wherein the set of gait dynamics includes a gait double-stance classification; and wherein quantifying a set of gait dynamics comprises: detecting ground contact time of right steps and left steps, detecting a double-stance condition in the ground contact time of the right and left steps, and thereby generating a gait double-stance classification.

12 . The method of claim 1 , further comprising collecting location data of the user; and wherein the fall risk assessment is further based on the location data, wherein the risk assessment model weighs the mobility metrics differently for different location data.

13 . The method of claim 1 , wherein the risk assessment model weighs the mobility metrics differently at different times of day.

14 . The method of claim 1 , further comprising collecting temperature data; and wherein the fall risk assessment is further based on the temperature data, wherein the risk assessment model weighs the mobility metrics based in part on the temperature data.

15 . The method of claim 1 , wherein the risk assessment model comprises a machine learning model in classification of risk in the fall risk assessment.

16 . A fall prevention system comprising:

a biomechanical sensing device that couples to a user and comprises at least an accelerometer, the sensing device being configured to collect sensor data; and

a processor configured to:

perform biomechanical analysis on the sensor data and generate mobility metrics of the user, wherein biomechanical analysis comprises configuration to: quantify a set of gait dynamics as a component of the mobility metrics, and generate a user activity graph as a component of the mobility metrics, wherein the activity graph characterizes activity states over a time period,

process the mobility metrics in a risk assessment model and generate a fall risk assessment, and

detect a trigger condition and trigger a response to the fall risk assessment.

17 . The system of claim 16 , further comprising a feedback interface, wherein the response to the fall risk assessment is user feedback of the current fall risk assessment that is communicated through the feedback interface.

18 . The system of claim 16 , wherein the user feedback is a rest recommendation.

19 . The system of claim 16 , wherein the gait dynamics comprises at least a stride shuffle metric, double-stance metric, and a tremor metric.

20 . The system of claim 16 , wherein the risk assessment model includes data inputs of location, time, and weather.

21 . The system of claim 16 , wherein the processor is further configured to measure the quality of patient mobility as reflected in the mobility metrics over an extended duration, and generate a rehabilitation progress report.