| name | motion-capture-analyzer |
| description | Motion capture data processing and analysis skill for gait analysis and biomechanical studies |
| allowed-tools | ["Read","Write","Glob","Grep","Edit","Bash"] |
| metadata | {"specialization":"biomedical-engineering","domain":"science","category":"Biomechanics and Structural Analysis","skill-id":"BME-SK-014"} |
Motion Capture Analyzer Skill
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
