BiomechGPT: Extending Motion-Language Models to Clinical Motion Understanding

Advances in markerless motion capture are making high-quality biomechanical data increasingly accessible, creating a growing need for scalable downstream analytics. Building a bespoke pipeline for each analysis task is time-consuming, motivating models that can flexibly handle diverse clinical questions within a single framework. Recent work has shown that fine-tuning language models to accept tokenized motion as an additional modality enables descriptive captioning of movement, raising the question of whether these models are also capable of clinically relevant motion understanding, where diverse tasks and annotations provide a natural testbed. We investigate whether such a multimodal motion--language model can answer detailed, clinically meaningful questions about movement. We collected 71 hours of biomechanical data from 750 participants, many with movement impairments, performing tasks commonly used in clinical assessment. To further expand the training dataset, we designed a cross-format tokenizer that directly encodes motion data from heterogeneous formats into a shared latent space without paired data, allowing a second dataset to be incorporated and enabling pooling annotations across datasets. From these tokenized representations, we constructed a multimodal dataset of motion-related question--answer pairs and used it to train BiomechGPT, a multimodal biomechanics--language model. BiomechGPT achieves competitive performance across a range of clinically relevant tasks, with performance scaling with both dataset and model size. It offers a new way for clinicians and researchers to interact with biomechanical data and represents a promising direction for rehabilitation-focused movement analysis. Project page: https://intelligentsensingandrehabilitation.github.io/BiomechGPT/