System and method for estimating human joint movements and controlling exoskeleton assistance

Abstract

A device may include a high-level control layer comprising a convolutional neural network (CNN) configured to receive exoskeleton sensor data from one or more sensors on an exoskeleton and generate a user state estimate. The device may include a mid-level control layer configured to receive the user state estimate and generate a torque command for an actuator based on the user state estimate. The user state estimate may be an estimated gait phase, where the mid-level control layer generates the torque command as a function of the estimated gait phase based on an assistance profile. The high-level control layer may include a backward labeler and a real-time adaptation trainer. The backward labeler relabels ground truth gait phase from the exoskeleton sensor data using a local peak detection. The real-time adaptation trainer trains the CNN in a single epoch of backpropagation with the ground truth gait phase.

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