Somatosensory Hoffmann Reflex (H-Reflex) Skill
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Somatosensory Hoffmann Reflex (H-Reflex) Skill
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"]Features
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-oxygenation
Quickstart
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"])License
Apache-2.0
This server cannot be deployed
Maintenance
Related MCP Connectors
Cardiopulmonary exercise test (CPET) analysis: ventilatory thresholds, metrics and substrate use.
Deterministic fitness coaching engine: adaptive programs, progression math, readiness autoregulation
Fuses biometric signals into a stress score (0-100) for AI adaptation. MCP + A2A native.
Affect analysis, 3D avatar params, empathy hints and somatic emotion decode.
Related MCP Servers
- AlicenseNot gradedqualityBmaintenanceEnables analysis of cutaneous laser Doppler thermal hyperemia, axon-reflex peaks, and endothelial nitric oxide bioavailability, generating CVC metrics, microvascular health scores, and classification guidance.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables evaluation of NIRS SmO2 desaturation profiles, muscle oxygen extraction, and hyperemic reperfusion recovery kinetics, with pacing recommendations.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables computational NIRS muscle oxygenation modeling, including SmO2 desaturation profiling, hyperemic reperfusion kinetics, and rest-to-work pacing recommendations through MCP tools.7MIT
- AlicenseNot gradedqualityBmaintenanceEnables deterministic evaluation of static and moving tactile spatial acuity thresholds, mechanoreceptive density estimates, and peripheral neuropathy-related sensory guidance. It converts discrimination-distance and response-ratio trial inputs into acuity scores and actionable recommendations.7MIT