Deep Learning Prediction of Gait Based on Inertial Measurements

Abstract

We report the application of recurrent deep learning networks, namely long term short memories (LSTM) for the modeling of gait synchronization of legs using a basic configuration of off-the-shelf inertial measurement units (IMU) providing six acceleration and rotation parameters. The proposed system copes with noisy and missing data due to high sampling rate, before applying the training of LSTM. We report accurate testing results on one experimental subject. This model can be transferred to robotised prostheses and assistive robotics devices in order to achieve quick stabilization and robust transfer of control algorithms to new users.