Motion Capture Analyzer Skill

SkillDev tools

Motion capture data processing and analysis skill for gait analysis and biomechanical studies

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Motion Capture Analyzer Skill skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/biomedical-engineering/skills/motion-capture-analyzer/SKILL.md and read by ahel’s review.

Purpose

The Motion Capture Analyzer Skill processes and analyzes motion capture data for gait analysis, biomechanical studies, and human factors research, supporting clinical evaluation and device validation.

Capabilities

Marker data processing and gap-filling
Inverse kinematics calculation
Ground reaction force analysis
Joint angle computation
Spatiotemporal parameter extraction
Statistical parametric mapping
Normative database comparison
Gait cycle segmentation
EMG synchronization
Multi-trial averaging
Variability analysis

Usage Guidelines

When to Use

Processing motion capture data
Conducting gait analysis studies
Validating orthopedic devices
Supporting clinical outcome assessments

Prerequisites

Motion capture data collected
Marker protocol documented
Calibration data available
Subject anthropometry recorded

Best Practices

Verify marker tracking quality
Apply appropriate filtering
Use validated biomechanical models
Compare with normative databases

Process Integration

This skill integrates with the following processes:

Gait Analysis and Musculoskeletal Modeling
Human Factors Engineering and Usability
Clinical Study Design and Execution
Orthopedic Implant Biomechanical Testing

Dependencies

Vicon Nexus
OptiTrack Motive
Visual3D
OpenSim
MATLAB/Python processing tools

Configuration

motion-capture-analyzer:
  data-types:
    - marker-trajectories
    - force-plate
    - EMG
    - pressure-mapping
  analysis-outputs:
    - joint-angles
    - joint-moments
    - joint-powers
    - spatiotemporal
  filtering:
    - butterworth
    - spline
    - moving-average

Output Artifacts

Processed marker trajectories
Joint kinematics
Kinetic data
Spatiotemporal parameters
Gait reports
Normative comparisons
Statistical analysis results
Visualization plots

Quality Criteria

Marker tracking gaps minimized
Filtering parameters appropriate
Model scaling accurate
Results validated against norms
Statistical analysis rigorous
Documentation supports clinical interpretation

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: motion-capture-analyzer
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter