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

TL;DR

This paper introduces a novel approach using large language models with prompt engineering to assess rehabilitation exercise quality and generate natural language feedback, enhancing virtual rehab platforms.

Contribution

It presents a new method that extracts exercise features from skeletal data and leverages LLMs with advanced prompting techniques for assessment and feedback in rehabilitation.

Findings

Effective exercise quality assessment demonstrated on public datasets.

LLMs successfully generate natural language feedback for patients.

Promising results in reasoning and evaluation accuracy.

Abstract

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.