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genpark-personal-agent-muscle-electromyography-root-mean-square-emg-skill

mcp
Guvenlik Denetimi
Uyari
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SUMMARY

Deterministic personal agent skill for evaluating surface electromyography (sEMG) RMS amplitude, percentage MVC, median frequency fatigue, and ergonomic muscular strain.

README.md

Surface Electromyography (sEMG) RMS Workload Skill

License: Apache-2.0
Python 3.10+
Zero Dependencies
MCP Protocol Ready
OpenClaw Certified

Zero-dependency sovereign personal agent skill for non-invasive evaluation of surface electromyography (sEMG) RMS amplitude, percentage MVC, median frequency fatigue, and occupational ergonomics.

flowchart TD
    SurfaceElectrodes["Bipolar Surface EMG Electrodes"] --> RMS_Filter["RMS Sliding-Window & MVC Normalization"]
    RMS_Filter --> Spectrum["Fast Fourier / Median Frequency (MDF) Extraction"]
    Spectrum --> Jonsson["Jonsson Ergonomic Static Load Modeling"]
    Jonsson --> StrainEngine["Neuromuscular Ergonomic Strain Engine"]
    StrainEngine --> Agent["OpenClaw / Hermes Personal Agent Actions"]

Features

  • Deterministic Math: Pure Python standard library implementation with zero external dependencies.
  • Biomechanical Ergonomics Standards: Implements validated Jonsson static muscular load thresholds and %MVC normalization.
  • Spectral Fatigue Shift Detection: Identifies localized intracellular acidification before subjective pain manifests.
  • Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.

Quickstart

from client import MuscleElectromyographyClient

client = MuscleElectromyographyClient()
result = client.evaluate_emg_workload(
    emg_rms_amplitude_uv=14.5,
    mvc_reference_uv=320.0,
    muscle_group="UPPER_TRAPEZIUS"
)
print("Activation:", result["mvc_percentage"], "% MVC")
print("Classification:", result["classification"])

License

Apache-2.0

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