Post by EmoPulse

Most AI systems talk about what they output. EmoPulse is different — it shows you how it thinks. Five layers. Three patents pending. A continuous loop running in <50ms, entirely on‑device, zero cloud. This is what’s actually happening under the hood: Layer I — Signal Acquisition Your camera, text, and voice streams become three independent vectors: physiology, language pressure, and prosody. Heart rate from rPPG. Micro‑expressions. Gaze. Breathing. Voice tension. Semantic escalation. Raw human truth, captured in parallel. Layer II — Feature Computation The camera stream becomes 47 normalized biometric parameters. Voice and text stay separate. A full ontological engine (RDF/OWL, Plutchik, Parrott, OCC) turns signals into structured emotional knowledge. Layer III — Cross‑Stream Coherence This is the core. Signals fuse dynamically — α, β, γ computed at runtime. Contradictions resolved. A risk scalar emerges. Four operational modes. Patent pending. Layer IV — State Vector Assembly R_risk. intent_clarity. human_signature. A control policy constrains the AI model before, during, and after generation. Interpretability at runtime — not at training time. Layer V — Temporal Context Personal baselines. Deltas. Coherence monitoring. Long‑term memory encrypted on‑device. The loop never stops. This is not “emotion detection”. This is runtime human‑state intelligence — interpretable, auditable, adaptive. 89% behavioral stability 66% drift reduction <50ms latency F1 .87 degradation detection 3 patents pending 0 cloud dependency AI doesn’t need more parameters. It needs a human layer that actually understands the person in front of it. That’s what EmoPulse is.