When to use
- For research protocol documentation and descriptive session summaries
- For preparing ConductVision Human Tracking video workflows
- For organizing export-ready methods text, filenames, or CSV quality checks
Generate a treadmill or overground running biomechanics video checklist for markerless research workflows.

Running biomechanics video checklist Protocol: overground-running Camera: fixed side-view camera Frame rate target: 60 fps Scale reference: in-frame calibration marker 1. Choose treadmill or overground capture before recording. 2. Keep the full body and both feet visible through several strides. 3. Use 60 fps or higher for foot-strike review when available. 4. Place calibration markers in the same plane as the runner. 5. Record several steady-state trials per condition. 6. Name files with participant, surface, speed, view, date, and trial. Running video setup was planned for markerless biomechanics research analysis. Research use only. This tool is for education, protocol documentation, and research workflow planning. It is not for care decisions, participant triage, or health advice.
When to use
Do not use for
Use participant, subject, trial, protocol, and research session language. Avoid care-directed interpretations.
Store source video, keypoint exports, confidence values, and session notes so outputs remain auditable.
Run one short pilot and inspect visibility, lighting, scale references, and keypoint confidence before collecting a full dataset.
The tool performs deterministic arithmetic, checklist generation, or CSV summary operations in the browser. It does not assess injuries, classify participants for care, or replace a protocol approved for research use.
Last validated 2026-06-02. Calculations are designed for planning and documentation support; verify procurement decisions against manufacturer specifications or institutional SOPs.
ConductScience Running Biomechanics Video Checklist (v0.10.253). ConductScience, Inc. 2026. Available at: https://conductscience.com/tools/running-biomechanics-video-checklist
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Mathis A et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat Neurosci. 2018;21(9):1281-1289.