Real-time Virtual Coach using LSTM for Assisting Physical Therapists with End-effector-based Robot-assisted Gait Training

With the development of robotic technology, the demand for state-of-the-art technology in the field of rehabilitation is rapidly increasing for the elderly and people with disabilities. In this paper, we propose a real-time virtual coach to assist physical therapists with the end-effector-based robot-assisted gait training for stroke survivors using Long Short-Term Memory (LSTM) networks. Our proposed virtual coach consists of the sensor module for data gathering and dataset generation, real-time classification of the pathologic patient gait during the training using LSTM networks, and delivery of the coaching recommendations in an audiovisual form. Our preliminary study determined the selection of coaching recommendations. LSTM networks are trained to provide the selected coaching recommendations. The performance of the proposed virtual coach is verified using classification simulation of an able-bodied person on the rehabilitation robot, G-EO System. The usability was verified through a satisfaction survey of five professional physical therapists.