Fall prediction based on electroencephalography and gait analysis data

Abstract

A method, a computer-readable storage device, and an apparatus for predicting a fall are disclosed. In one example, a method performed by a processor deployed in a communications network includes collecting gait information associated with a user from a first wearable device worn by the user, collecting electroencephalography information associated with the user from a second wearable device worn by the user, calculating a likelihood that the user will fall within a threshold period of time from a current time, wherein the calculating is based on a combination of the gait information and the electroencephalography information; and sending an instruction to an endpoint device associated with the user when the likelihood exceeds a predefined threshold, wherein the instruction instructs the endpoint device to generate an alert alerting the user that a fall is likely.

What is claimed is: 
     
         1 . A method comprising:
 collecting, by a processor deployed in a communications network, gait information associated with a user from a first wearable device worn by the user;   collecting, by the processor, electroencephalography information associated with the user from a second wearable device worn by the user;   calculating, by the processor, a likelihood that the user will fall within a threshold period of time from a current time, wherein the calculating is based on a combination of the gait information and the electroencephalography information; and   sending, by the processor, an instruction to an endpoint device associated with the user when the likelihood exceeds a predefined threshold, wherein the instruction instructs the endpoint device to generate an alert alerting the user that a fall is likely.   
     
     
         2 . The method of  claim 1 , wherein the first wearable device comprises an insole worn in a shoe of the user. 
     
     
         3 . The method of  claim 2 , wherein the insole comprises a plurality of force resisting sensors. 
     
     
         4 . The method of  claim 1 , wherein the second wearable device comprises an electroencephalography headset worn on a head of the user. 
     
     
         5 . The method of  claim 4 , wherein the electroencephalography headset comprises a plurality of electrodes. 
     
     
         6 . The method of  claim 1 , wherein at least one of the first wearable device and the second wearable device includes a notification means for generating the alert. 
     
     
         7 . The method of  claim 1 , wherein the gait information comprises a raw measurement of at least one of: pressure information, acceleration information, gyroscopic information, elevation information, a time, a temperature, and a fluid content. 
     
     
         8 . The method of  claim 1 , wherein the electroencephalography information comprises a raw measurement of at least one of: brain electrical activity, acceleration information, gyroscopic information, elevation information, a time, and a location. 
     
     
         9 . The method of  claim 1 , wherein the calculating the likelihood comprises:
 calculating a first difference between a motion characteristic of the user and a baseline for the motion characteristic, using the gait information;   calculating a second difference between a neurological activity characteristic of the user and a baseline for the neurological activity characteristic, using the electroencephalography information; and   quantifying the likelihood based on at least the first difference and the second difference.   
     
     
         10 . The method of  claim 9 , wherein the calculating the likelihood further accounts for at least one physical characteristic of the user. 
     
     
         11 . The method of  claim 10 , wherein the at least one physical characteristic comprises at least one of: a height of the user, a weight of the user, a body mass index of the user, an age of the user, and a known medical condition of the user. 
     
     
         12 . The method of  claim 9 , wherein the motion characteristic comprises a stride length. 
     
     
         13 . The method of  claim 9 , wherein the motion characteristic comprises a speed. 
     
     
         14 . The method of  claim 9 , wherein the motion characteristic comprises an acceleration. 
     
     
         15 . The method of  claim 9 , wherein the motion characteristic comprises an elevation. 
     
     
         16 . The method of  claim 9 , wherein the baseline for the motion characteristic is based on a determined mode of motion of the user. 
     
     
         17 . The method of  claim 9 , wherein the baseline for the motion characteristic is user-specific. 
     
     
         18 . The method of  claim 9 , wherein the neurological activity characteristic comprises neural oscillations. 
     
     
         19 . A non-transitory computer-readable storage device storing instructions which, when executed by a processor deployed in a communication network, cause the processor to perform operations, the operations comprising:
 collecting gait information associated with a user from a first wearable device worn by the user;   collecting electroencephalography information associated with the user from a second wearable device worn by the user;   calculating a likelihood that the user will fall within a threshold period of time from a current time, wherein the calculating is based on a combination of the gait information and the electroencephalography information; and   sending an instruction to an endpoint device associated with the user when the likelihood exceeds a predefined threshold, wherein the instruction instructs the endpoint device to generate an alert alerting the user that a fall is likely.   
     
     
         20 . An apparatus comprising:
 a processor deployed in a communication network; and   a computer-readable medium storing instructions which, when executed by the processor, cause the processor to perform operations, the operations comprising:
 collecting gait information associated with a user from a first wearable device worn by the user; 
 collecting electroencephalography information associated with the user from a second wearable device worn by the user; 
 calculating a likelihood that the user will fall within a threshold period of time from a current time, wherein the calculating is based on a combination of the gait information and the electroencephalography information; and 
 sending an instruction to an endpoint device associated with the user when the likelihood exceeds a predefined threshold, wherein the instruction instructs the endpoint device to generate an alert alerting the user that a fall is likely.