Abstract: A gait analysis system, which includes a neural network with a recurrent neural network layer and a fully connected layer, that receives sensor data indicative of an individual's gait and outputs an assessment regarding the individual's health. The neural network is trained using training data indicative of abnormal gaits and normal gaits. To analyze the training data and the sensor data, the recurrent neural network layer parses each piece of data into a series of windows and analyzes each window in series to generate a context vector characterizing each window and the previously analyzed windows. The fully connected layer, having been trained to differentiate between normal gaits and abnormal gaits based on context vectors characterizing the training data, is used to generate a final assessment characterizing the user gate as normal or abnormal using one or more of the context vectors characterizing the sensor data.

Abstract: A gait analysis system, which includes a neural network with a recurrent neural network layer and a fully connected layer, that receives sensor data indicative of an individual's gait and outputs an assessment regarding the individual's health. The neural network is trained using training data indicative of abnormal gaits and normal gaits. To analyze the training data and the sensor data, the recurrent neural network layer parses each piece of data into a series of windows and analyzes each window in series to generate a context vector characterizing each window and the previously analyzed windows. The fully connected layer, having been trained to differentiate between normal gaits and abnormal gaits based on context vectors characterizing the training data, is used to generate a final assessment characterizing the user gate as normal or abnormal using one or more of the context vectors characterizing the sensor data.

Abstract: A gait analysis system, which includes a neural network with a recurrent neural network layer and a fully connected layer, that receives sensor data indicative of an individual's gait and outputs an assessment regarding the individual's health. The neural network is trained using training data indicative of abnormal gaits and normal gaits. To analyze the training data and the sensor data, the recurrent neural network layer parses each piece of data into a series of windows and analyzes each window in series to generate a context vector characterizing each window and the previously analyzed windows. The fully connected layer, having been trained to differentiate between normal gaits and abnormal gaits based on context vectors characterizing the training data, is used to generate a final assessment characterizing the user gate as normal or abnormal using one or more of the context vectors characterizing the sensor data.

Abstract: A device and method of use for treating a medical disorder by surgically implanting into a patient at least one sensor element capable of detecting and conveying cell signals; attaching a management unit such that a micro controller of the management unit is connected to at least one sensor element; and connecting the management unit via a lead bundle to at least one treatment device. The treatment device may be an electrical stimulation device, a magnetic stimulation device, a heat transfer device, or a medication delivery device. Responsive to signals from the one or more sensor elements, mathematical algorithms of the management unit use wavelet crosscorrelation analysis to prompt delivery of at least one treatment modality, such heat transfer, current pulses, magnetic stimulation or medication. The medical disorder may arise from the brain, central nervous system or organs and tissues outside of the central nervous system.