  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.
