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.

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.

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.