genpark-personal-agent-postural-sway-center-of-pressure-skill
mcp
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Deterministic personal agent skill for analyzing Center of Pressure (COP) statokinesigram trajectory, 95% confidence ellipse area, sway velocity, and Romberg Quotient.
README.md
Postural Sway & Center of Pressure (COP) Statokinesigram Skill
A sovereign personal agent capability for computational posturography, processing 2D Center of Pressure (COP) statokinesigram trajectories, 95% confidence ellipse area, and Romberg Quotient.
Architecture
flowchart TD
ForcePlate["COP Trajectory: Mediolateral (x) & Anterior-Posterior (y)"] --> SpatialCov["Spatial Bivariate Covariance Matrix"]
SpatialCov --> Eigenvalues["Eigenvalue Decomposition (lambda_1, lambda_2)"]
Eigenvalues --> Ellipse["95% Confidence Ellipse Area (CEA_95)"]
ForcePlate --> Kinematics["Path Length (mm) & Mean Velocity (mm/s)"]
Ellipse & Kinematics --> PSI["Postural Stability Index & Fall Risk Tier"]
PSI --> Romberg["Romberg Quotient (EO vs EC Sensory Re-weighting)"]
Quickstart
from client import PosturalSwayClient
client = PosturalSwayClient()
# Analyze a series of COP displacements (mm)
x_ml = [0.2, 0.5, 0.8, 0.4, -0.1, -0.4, -0.2, 0.1]
y_ap = [1.2, 1.5, 1.1, 0.7, 0.3, -0.2, 0.4, 0.9]
sway = client.analyze_cop_sway(x_ml, y_ap, sample_rate_hz=50.0)
print("Mean Sway Velocity:", sway["mean_sway_velocity_mm_s"], "mm/s")
print("95% Ellipse Area:", sway["confidence_ellipse_area_95_mm2"], "mm²")
print("Postural Stability Index:", sway["postural_stability_index"], "/ 100")
MCP Server Integration
Add to your claude_desktop_config.json or OpenClaw tool registry:
{
"mcpServers": {
"postural-sway": {
"command": "python",
"args": ["/path/to/genpark-personal-agent-postural-sway-center-of-pressure-skill/mcp_server.py"]
}
}
}
License
MIT License.
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