4 papers
BiomechAgent: AI-Assisted Biomechanical Analysis Through Code-Generating Agents
R. James Cotton, Thomas Leonard
Markerless motion capture is making quantitative movement analysis increasingly accessible, yet analyzing the resulting data remains a barrier for clinicians without programming ex…
Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning
Sara Rajaram, R. James Cotton, Fabian H. Sinz
Preference-based Reinforcement Learning (PbRL) entails a variety of approaches for aligning models with human intent to alleviate the burden of reward engineering. However, most pr…
KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capture
R. James Cotton
Broader access to high-quality movement analysis could greatly benefit movement science and rehabilitation, such as allowing more detailed characterization of movement impairments…
BiomechGPT: Extending Motion-Language Models to Clinical Motion Understanding
Ruize Yang, Ann Kennedy, R. James Cotton
Advances in markerless motion capture are making high-quality biomechanical data increasingly accessible, creating a growing need for scalable downstream analytics. Building a besp…