genpark-personal-agent-muscle-mechanomyography-mmg-stiffness-skill

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SUMMARY

Deterministic personal agent skill for evaluating muscle mechanomyography (MMG), electromechanical delay (EMD), and peripheral muscular fatigue kinetics.

README.md

Muscle Mechanomyography (MMG) & Contractile Stiffness Skill

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

Zero-dependency sovereign personal agent skill for non-invasive analysis of Muscle Mechanomyography (MMG), electromechanical delay (EMD), and skeletal muscle fatigue kinetics.

flowchart TD
    AcousticSensor["Piezoelectric / Accelerometer MMG Sensors"] --> Vibration["Lateral Intramuscular Pressure Waves"]
    Vibration --> Spectral["Time-Frequency Processing (RMS, MPF, EMD)"]
    Spectral --> ContractileScore["Muscular Contractile Dynamic Engine"]
    ContractileScore --> Agent["OpenClaw / Hermes Personal Agent Actions"]

Features

  • Deterministic Math: Pure Python standard library implementation with zero external dependencies.
  • Biomechanical Standard Metrics: Implements validated equations for mechanomyographic amplitude and spectral power frequency.
  • Peripheral Muscular Fatigue Stratification: Quantitative evaluation of excitation-contraction uncoupling and cross-bridge kinetics.
  • Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.

Quickstart

from client import MuscleMechanomyographyClient

client = MuscleMechanomyographyClient()
result = client.evaluate_mechanomyography(
    mmg_rms_amplitude_mv=1.92,
    mmg_mean_power_frequency_hz=27.4,
    electromechanical_delay_ms=33.5
)
print("Contractile Score:", result["muscular_contractile_score"])
print("Classification:", result["classification"])

License

Apache-2.0

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