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 address the challenges of assessing home-based rehabilitation programmes. First, we propose a comprehensive home-based rehabilitation suite integrating a live-feedback module and an advanced deep-learning-based movement quality assessment (MQA) model. The live-feedback module provides real-time guidance through intuitive color cues, enhancing exercise correctness and reducing injury risks. The deep-learning model evaluates the overall exercise performance and gives real-valued movement quality assessment scores. We investigate the 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 the 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 an order of magnitude. Finally, an extensive ablation study is carried out to assess the effectiveness of each building block in the network.

Second, we explore an attention-guided transformer-based architecture for MQA. A comparative analysis against the current state-of-the-art methods is undertaken to establish the validity of the proposed model. Further, we show that the proposed model offers significant performance improvement in training and inference time, which is pivotal for any real-time system. Finally, we show that analysis of the attention maps of the proposed model can give critical insights into the decision-making process of the deep-learning model, thus improving the overall interpretability of predicted assessment scores.

The methods developed in our work can be utilized to offer cost-effective assessment within the comfort of individuals' homes, thereby enhancing the overall outcomes of at-home physical rehabilitation routines.