# A Review of Computational Approaches for Evaluation of Rehabilitation Exercises

arXiv: https://arxiv.org/abs/2003.08767 · HTML: https://mlsanity.com/paper/2003.08767/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation](https://mlsanity.com/paper/2401.06150/) (2023)
- [Differentiable Biomechanics for Markerless Motion Capture in Upper Limb Stroke Rehabilitation: A Comparison with Optical Motion Capture](https://mlsanity.com/paper/2411.14992/) (2024)
- [A Medical Low-Back Pain Physical Rehabilitation Dataset for Human Body Movement Analysis](https://mlsanity.com/paper/2407.00521/) (2024)
- [Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints](https://mlsanity.com/paper/2304.09735/) (2023)
- [Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise Quality Assessment](https://mlsanity.com/paper/2306.09546/) (2023)
- [A Standardized Benchmark for Skeleton-Based Rehabilitation Assessment Using Deep Learning](https://mlsanity.com/paper/2507.21018/) (2025)
- [Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives](https://mlsanity.com/paper/2403.02772/) (2024)
- [Analyzing Data Efficiency and Performance of Machine Learning Algorithms for Assessing Low Back Pain Physical Rehabilitation Exercises](https://mlsanity.com/paper/2408.02855/) (2024)
- [Unsupervised Motion Retargeting for Human-Robot Imitation](https://mlsanity.com/paper/2402.05115/) (2024)

## Abstract (arXiv)

Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been conducive to the development of approaches for automated assessment of patient performance and progress toward functional recovery. The presented study reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. Such approaches will play an important role in supplementing traditional rehabilitation assessment performed by trained clinicians, and in assisting patients participating in home-based rehabilitation. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches. The review places an emphasis on the application of machine learning methods for movement evaluation in rehabilitation. Related work in the literature on data representation, feature engineering, movement segmentation, and scoring functions is presented. The study also reviews existing sensors for capturing rehabilitation movements and provides an informative listing of pertinent benchmark datasets. The significance of this paper is in being the first to provide a comprehensive review of computational methods for evaluation of patient performance in rehabilitation programs.