# Rehabilitation Exercise Quality Assessment and Feedback Generation Using Large Language Models with Prompt Engineering

arXiv: https://arxiv.org/abs/2505.18412 · HTML: https://mlsanity.com/paper/2505.18412/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [Cycles of Thought: Measuring LLM Confidence through Stable Explanations](https://mlsanity.com/paper/2406.03441/) (2024)
- [A Deep Learning Framework for Assessing Physical Rehabilitation Exercises](https://mlsanity.com/paper/1901.10435/) (2019)
- [Probabilistic Medical Predictions of Large Language Models](https://mlsanity.com/paper/2408.11316/) (2024)
- [Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices](https://mlsanity.com/paper/2405.01249/) (2024)
- [Cross-Modal Video to Body-joints Augmentation for Rehabilitation Exercise Quality Assessment](https://mlsanity.com/paper/2306.09546/) (2023)
- [Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives](https://mlsanity.com/paper/2403.02772/) (2024)

## Built on by (most cited first, 3 of 3)

- [ChestGPT: Integrating Large Language Models and Vision Transformers for Disease Detection and Localization in Chest X-Rays](https://mlsanity.com/paper/2507.03739/) (2025)
- [Retrieval-Augmented Large Language Models for Evidence-Informed Guidance on Cannabidiol Use in Older Adults](https://mlsanity.com/paper/2604.09548/) (2026)
- [When Does Exercise-Specific Joint Selection Help? An Audit of Evaluation and Control Design](https://mlsanity.com/paper/2610.01188/) (2026)

## Abstract (arXiv)

Exercise-based rehabilitation improves quality of life and reduces morbidity, mortality, and rehospitalization, though transportation constraints and staff shortages lead to high dropout rates from rehabilitation programs. Virtual platforms enable patients to complete prescribed exercises at home, while AI algorithms analyze performance, deliver feedback, and update clinicians. Although many studies have developed machine learning and deep learning models for exercise quality assessment, few have explored the use of large language models (LLMs) for feedback and are limited by the lack of rehabilitation datasets containing textual feedback. In this paper, we propose a new method in which exercise-specific features are extracted from the skeletal joints of patients performing rehabilitation exercises and fed into pre-trained LLMs. Using a range of prompting techniques, such as zero-shot, few-shot, chain-of-thought, and role-play prompting, LLMs are leveraged to evaluate exercise quality and provide feedback in natural language to help patients improve their movements. The method was evaluated through extensive experiments on two publicly available rehabilitation exercise assessment datasets (UI-PRMD and REHAB24-6) and showed promising results in exercise assessment, reasoning, and feedback generation. This approach can be integrated into virtual rehabilitation platforms to help patients perform exercises correctly, support recovery, and improve health outcomes.