Tele-EvalNet: A Low-Cost, Teleconsultation System for Home Based Rehabilitation of Stroke Survivors Using Multiscale CNN-ConvLSTM Architecture

Abstract

Home-based physical-rehabilitation programmes make up a significant portion of all physical rehabilitation programmes. Due to the absence of clinical supervision during home-based sessions, corrective feedback and movement quality evaluation are of utmost importance. We propose a complete home-based rehabilitation suite consisting of 1) a live-feedback module and 2) a deep-learning based movement quality assessment model. The live feedback module provides real-time feedback on a patient’s exercise performance with easy-to-understand color cues. The deep-learning model evaluates the overall exercise performance and gives real-valued movement quality assessment scores. In this paper, we investigate role of the following components in designing the deep-learning model: 1) clinically guided features, 2) special activation functions, 3) multi-scale convolutional architecture, and 4) context windows. Compared to current state-of-the-art deep-learning methods for assessing movement quality, improved performance on a standard physical rehabilitation dataset KIMORE with 78 subjects is reported. Performance improvement is coupled with a drastic reduction in parameter size and inference time of the model by atleast an order of magnitude. Therefore, making real-time feedback to the subjects possible. Finally, an extensive ablation study is carried out to assess the effectiveness of each building block in the network.