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Zero-dependency sovereign personal agent skill for non-invasive evaluation of Distortion Product Otoacoustic Emissions (DPOAE), cochlear outer hair cell biomechanics, and headphone noise damage.
flowchart TD
DualTones["Primary Pure Tones (F1: 65 dB, F2: 55 dB)"] --> EarCanalProbe["Low-Noise Sensitive Miniature Microphone"]
EarCanalProbe --> SpectralAnalysis["FFT Extraction of 2F1 - F2 Distortion Peak"]
SpectralAnalysis --> SNR_Engine["Signal-to-Noise Ratio & Clinical Pass Logic"]
SNR_Engine --> CochlearScore["Cochlear Outer Hair Cell Resilience Engine"]
CochlearScore --> Agent["OpenClaw / Hermes Personal Agent Actions"]Deterministic Math: Pure Python standard library implementation with zero external dependencies.
Clinical Audiological Standards: Implements clinical pass criteria ($SNR \ge 6 \text{ dB}$, $Amp \ge -5 \text{ dB SPL}$).
Early Noise Overexposure Detection: Identifies outer hair cell fatigue before pure-tone hearing threshold loss occurs.
Model Context Protocol (MCP): Native JSON-RPC tool integration for LLMs and autonomous agents.
Related MCP server: vHIT Vestibular Gain MCP Server
from client import AcousticOtoacousticEmissionsClient
client = AcousticOtoacousticEmissionsClient()
result = client.evaluate_dpoae(
f2_frequency_hz=2000.0,
dpoae_amplitude_db_spl=8.5,
noise_floor_db_spl=-12.5
)
print("DPOAE SNR:", result["signal_to_noise_ratio_snr_db"], "dB")
print("Classification:", result["classification"])Apache-2.0
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