Publication
Exploring Large Language Models for Automated Gait Analysis
Rebecca Keilhauer; Michael Lorenz; Carlo Dindorf; Stefan Ernst; Chen-Yu Wang; Paul Messer; Didier Stricker
In: Proceedings of 2nd International Conference of AIxHMC 2025. International Conference on Artificial Intelligence for Medicine, Health, and Care (AIxMHC-2025), October 13-15, Taichung, Taiwan, Province of China, IEEE Xplore, 2025.
Abstract
Biomechanical gait analysis is a critical tool in
orthopedic diagnosis and rehabilitation, particularly for patients
undergoing total knee arthroplasty. Traditional assessments,
however, are time-intensive, subjective, and reliant on expert
interpretation. In this study, we investigate the use of large
language models (LLMs), specifically ChatGPT-4o, to generate
clinically relevant gait assessments based on spatiotemporal mo-
tion data. We collected gait recordings from 11 preoperative knee
arthroplasty patients and obtained expert annotations from phys-
iotherapists. Using a structured prompt engineering approach,
we enabled ChatGPT-4o to produce full-body gait descriptions,
which were then evaluated in a blinded study by physiotherapy
students and experts teaching gait analysis. Our results show that
ChatGPT-4o achieves comparable levels of correctness and clarity
to human-generated assessment. Nonetheless, limitations such as
the absence of pathological context, and evaluator variability
highlight the need for further refinement. This work presents
a proof of concept for integrating generative AI into clinical
gait analysis and underscores its potential as an assistive tool in
physiotherapy and orthopedic diagnostics. Additionally we make
the data publicly available.
