Tele-EvalNet: A Low-cost, Teleconsultation System for Home based Rehabilitation of Stroke Survivors using Multiscale CNN-LSTM Architecture

TL;DR

Tele-EvalNet is a low-cost teleconsultation system that uses a multiscale CNN-LSTM architecture to provide real-time feedback and performance evaluation for stroke rehabilitation at home, aiming to improve outcomes and reduce costs.

Contribution

It introduces a novel multiscale CNN-LSTM model for performance scoring and a dual-component system for real-time feedback and assessment in stroke rehabilitation.

Findings

Outperforms existing rehabilitation models in score prediction accuracy

Provides effective real-time feedback on exercise correctness

Demonstrates clinical validation and robustness

Abstract

Technology has an important role to play in the field of Rehabilitation, improving patient outcomes and reducing healthcare costs. However, existing approaches lack clinical validation, robustness and ease of use. We propose Tele-EvalNet, a novel system consisting of two components: a live feedback model and an overall performance evaluation model. The live feedback model demonstrates feedback on exercise correctness with easy to understand instructions highlighted using color markers. The overall performance evaluation model learns a mapping of joint data to scores, given to the performance by clinicians. The model does this by extracting clinically approved features from joint data. Further, these features are encoded to a lower dimensional space with an autoencoder. A novel multi-scale CNN-LSTM network is proposed to learn a mapping of performance data to the scores by leveraging features extracted at multiple scales. The proposed system shows a high degree of improvement in score predictions and outperforms the state-of-the-art rehabilitation models.