A Point Cloud Transformer for Remote Monitoring and Automated Assessment of Physical Rehabilitation Exercises

2026-06-29

AI Summary

This work addresses key limitations in existing automated rehabilitation assessment methods—such as insufficient feature extraction, high preprocessing costs, and neglect of critical joints—by proposing a novel point cloud Transformer framework based on RGBD data. The approach integrates curve-aware feature enhancement and axial self-attention mechanisms to effectively aggregate joint motion information, enabling precise identification of key joints and their functional roles during movement. Designed with a lightweight architecture, the model supports efficient inference and is suitable for home-based deployment. Evaluated on three benchmark datasets (Kimore, UI-PRMD, and IRDS), it outperforms current state-of-the-art methods, demonstrating superior interpretability, generalization capability, and sensitivity to subtle differences in specific joints across similar actions.

Abstract

Rehabilitation exercises are essential in restoring lost physical functions of patients suffering from various diseases (e.g., Parkinson's, back pain). Carrying out these rehabilitation exercises, often prescribed by health experts, is costly, unavailable, and requires expert supervision. The availability of RGBD images and movement/position data of joints along with expert annotation of exercise data has prompted the use of automatic assessment of the quality of rehabilitation exercises, which is cost-effective and can be carried out at home. However, existing approaches do not extract relevant features, lack practical application, require expensive pre-processing, or overlook crucial features. This study proposes a transformer-based framework for point clouds to extract features and assess rehabilitation exercises by analyzing joint positions collected through RGBD data. We adapt and utilize a curve-based point-cloud feature aggregation technique to augment point-cloud information that aids model output. The transformer architecture also uses axial self-attention, recognizing important joints and their roles to assist users in performing the exercise better. The guided system outperforms existing approaches and is also practically relevant due to its small size, fast inference, and generalization on specific joints in similar exercises. We conduct our experiments on three crucial baseline datasets for rehabilitation exercises: Kimore, UI-PRMD, and IRDS.

Problem

• rehabilitation exercise assessment
• point cloud
• remote monitoring
• feature extraction
• RGBD data

Innovation

• Point Cloud Transformer
• Axial Self-Attention
• Curve-based Feature Aggregation
• Rehabilitation Exercise Assessment
• RGBD-based Motion Analysis