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Alpha-Park

Video Head Impulse Test (vHIT) & Vestibular Gain Skill

by Alpha-Park

Video Head Impulse Test (vHIT) & Vestibular Gain 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 video Head Impulse Test (vHIT) angular vestibulo-ocular reflex (VOR) gain, overt/covert saccade quantification, and gaze stability.

flowchart TD
    HeadMotion["High-Frequency Angular Head Impulse (>150 deg/s)"] --> OpticalPupil["Pupil & Eye Compensatory Slow Phase"]
    OpticalPupil --> VOR_Math["VOR Gain Calculation (Eye Vel / Head Vel)"]
    VOR_Math --> SaccadeAudit["Catch-Up Saccade Area & Gaze Stability Engine"]
    SaccadeAudit --> Agent["OpenClaw / Hermes Personal Agent Actions"]

Features

  • Deterministic Math: Pure Python standard library implementation with zero external dependencies.

  • Clinical Neuro-Otology Standards: Implements validated algorithms for high-frequency angular canal excitation.

  • Dynamic Visual Acuity & Motion Sickness Stratification: Direct quantification of retinal slip and vestibular compensation.

  • Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.

Related MCP server: Pupillary Light Reflex MCP Server

Quickstart

from client import VestibularHeadImpulseClient

client = VestibularHeadImpulseClient()
result = client.evaluate_vhit(
    head_velocity_deg_s=195.0,
    eye_velocity_deg_s=188.0,
    overt_saccades_count=0
)
print("VOR Gain:", result["vor_gain"])
print("Classification:", result["classification"])

License

Apache-2.0

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