# A co-design approach for a rehabilitation robot coach for physical rehabilitation based on the error classification of motion errors

arXiv: https://arxiv.org/abs/2111.09729 · HTML: https://mlsanity.com/paper/2111.09729/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2021.

## Built on by (most cited first, 5 of 5)

- [A Medical Low-Back Pain Physical Rehabilitation Dataset for Human Body Movement Analysis](https://mlsanity.com/paper/2407.00521/) (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)
- [Skeleton-Based Transformer for Classification of Errors and Better Feedback in Low Back Pain Physical Rehabilitation Exercises](https://mlsanity.com/paper/2504.13866/) (2025)
- [Non-parametric regression for robot learning on manifolds](https://mlsanity.com/paper/2310.19561/) (2023)
- [Unsupervised Motion Retargeting for Human-Robot Imitation](https://mlsanity.com/paper/2402.05115/) (2024)

## Abstract (arXiv)

The rising number of the elderly incurs growing concern about healthcare, and in particular rehabilitation healthcare. Assistive technology and assistive robotics in particular may help to improve this process. We develop a robot coach capable of demonstrating rehabilitation exercises to patients, watch a patient carry out the exercises and give him feedback so as to improve his performance and encourage him. The HRI of the system is based on our study with a team of rehabilitation therapists and with the target population.The system relies on human motion analysis. We develop a method for learning a probabilistic representation of ideal movements from expert demonstrations. A Gaussian Mixture Model is employed from position and orientation features captured using a Microsoft Kinect v2. For assessing patients' movements, we propose a real-time multi-level analysis to both temporally and spatially identify and explain body part errors. This analysis combined with a classification algorithm allows the robot to provide coaching advice to make the patient improve his movements. The evaluation on three rehabilitation exercises shows the potential of the proposed approach for learning and assessing kinaesthetic movements.