# BiomechAgent: AI-Assisted Biomechanical Analysis Through Code-Generating Agents

arXiv: https://arxiv.org/abs/2602.06975 · HTML: https://mlsanity.com/paper/2602.06975/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 24 of 24)

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- [DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines](https://mlsanity.com/paper/2312.13382/) (2023)
- [Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capture](https://mlsanity.com/paper/2402.17192/) (2024)
- [Improved Trajectory Reconstruction for Markerless Pose Estimation](https://mlsanity.com/paper/2303.02413/) (2023)
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- [PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research](https://mlsanity.com/paper/2203.08792/) (2022)
- [Transforming Gait: Video-Based Spatiotemporal Gait Analysis](https://mlsanity.com/paper/2203.09371/) (2022)
- [Portable Biomechanics Laboratory: Clinically Accessible Movement Analysis from a Handheld Smartphone](https://mlsanity.com/paper/2507.08268/) (2025)
- [KinTwin: Imitation Learning with Torque and Muscle Driven Biomechanical Models Enables Precise Replication of Able-Bodied and Impaired Movement from Markerless Motion Capture](https://mlsanity.com/paper/2505.13436/) (2025)
- [A Causal Framework for Precision Rehabilitation](https://mlsanity.com/paper/2411.03919/) (2024)

## Abstract (arXiv)

Markerless motion capture is making quantitative movement analysis increasingly accessible, yet analyzing the resulting data remains a barrier for clinicians without programming expertise. We present BiomechAgent, a code-generating AI agent that enables biomechanical analysis through natural language and allows users to querying databases, generating visualizations, and even interpret data without requiring users to write code. To evaluate BiomechAgent's capabilities, we developed a systematic benchmark spanning data retrieval, visualization, activity classification, temporal segmentation, and clinical reasoning. BiomechAgent achieved robust accuracy on data retrieval and visualization tasks and demonstrated emerging clinical reasoning capabilities. We used our dataset to systematically evaluate several of our design decisions. Biomechanically-informed, domain-specific instructions significantly improved performance over generic prompts, and integrating validated specialized tools for gait event detection substantially boosted accuracy on challenging spatiotemporal analysis where the base agent struggled. We also tested BiomechAgent using a local open-weight model instead of a frontier cloud based LLM and found that perform was substantially diminished in most domains other than database retrieval. In short, BiomechAgent makes the data from accessible motion capture and much more useful and accessible to end users.