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Zero-dependency sovereign personal agent skill for non-invasive evaluation of Hoffmann Reflex (H-Reflex) $H_{max} / M_{max}$ recruitment ratio, reflex onset latency, and spinal alpha motoneuron pool neuromuscular excitability.
flowchart TD
IaStim["Submaximal Ia Afferent Electrical Stimulation (Tibial Nerve)"] --> MonoSynapse["Spinal Cord Monosynaptic Synapse (Ventral Horn)"]
MonoSynapse --> AlphaMotoneuron["Alpha Motoneuron Pool Recruitment"]
AlphaMotoneuron --> H_Wave["Soleus EMG H-Reflex Wave (Latency ~30 ms)"]
MaxDirectStim["Supramaximal Direct Motor Axon Stim"] --> M_Wave["Maximal Direct Muscle M-Wave"]
H_Wave & M_Wave --> RatioCalc["Hmax / Mmax Ratio Engine & Central Fatigue Score"]
RatioCalc --> Agent["OpenClaw / Hermes Personal Agent Recovery Guarding"]Deterministic Math: Pure Python standard library implementation with zero external dependencies.
Electroneuromyography (ENMG) Standards: Formulates Hmax/Mmax recruitment curves, monosynaptic transmission latency, and presynaptic inhibition.
Central Neuromuscular Fatigue: Highly sensitive detection of spinal motoneuron exhaustion vs peripheral muscle fatigue.
Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.
Related MCP server: Muscle Mechanomyography (MMG) & Contractile Stiffness Skill
from client import SomatosensoryHReflexClient
client = SomatosensoryHReflexClient()
result = client.evaluate_h_reflex(
h_max_amplitude_mv=4.2,
m_max_amplitude_mv=7.8,
h_latency_ms=30.4
)
print("Hmax / Mmax Ratio:", result["h_m_ratio_pct"], "%")
print("Spinal Excitability Score:", result["spinal_excitability_score"])
print("Classification:", result["classification"])Apache-2.0
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