A New Computing Layer, Built on the Human Body
Synheart’s technology transforms raw physiological signals into meaningful measures of human state — including emotion, focus, cognitive load, and behavioral patterns — through a unified Human-State Interface (HSI). Our system combines advanced biosignal processing, multimodal fusion, and ultra-light edge models to deliver real-time state inference privately, ethically, and directly on-device.
From Signals to Human State
Synheart's architecture is a modular pipeline designed to convert multimodal biosignals into interpretable human-state outputs — without sending personal data to the cloud.
Collection
Collect normalized biosignals (HR, HRV, PPG, EDA, ACC) from wearable platforms and unified device APIs.
Inference
Run our on-device HSI models to derive emotional state, focus load, cognitive effort, and motion-behavior cues in real time.
Fusion
Combine biosignals, motion patterns, and contextual cues into state-level scores (E-scores, Focus Levels, Behavioral Indicators).
Reflection
Feed insights back into apps, agents, or analytics — enabling personalized interactions, UX research, adaptive systems, and Affective AI.
The Language of the Body
Human-State Intelligence (HSI) is built on the science of mapping physiological rhythms to emotional, cognitive, and behavioral states. Our models analyze fluctuations in HRV, EDA peaks, BVP amplitude, and motion signatures to understand patterns such as:
Stress vs calm
Amusement vs engagement
Cognitive effort & attention
Micro-behavioral signals (stillness, restlessness, tilt, pacing)
Technical Highlights
Real-time HRV & frequency-domain feature extraction
Motion noise correction & artifact filtering
EDA peak-rate modeling
Multimodal fusion (HR + HRV + EDA + Motion)
Edge-optimized neural networks (BiLSTM, CNN Hybrid)
"Every Heartbeat Carries Emotional Context We Just Needed The Tools To Listen."
Privacy by Design. Performance by Science.
Our inference engine runs entirely locally on wearables and mobile devices. No raw biosignals, identifiers, or emotional labels ever leave the device.
Key Technologies
HRV-Based Neural Networks
BiLSTM, CNN hybrid architectures
Lightweight Edge Architectures (<5MB models)
Optimized for mobile and wearable devices
Cross-Platform Compatibility (TF Lite, Core ML, ONNX)
Works across all major platforms
Emotion & Focus Sandboxing (on-device protection)
Isolated processing environment
Benefits
No Data Leaves Device
Raw biosignals stay local
Millisecond-Level Inference
Real-time processing
Adaptive Learning Without Personal Data
Privacy-preserving personalization
Auditable, Transparent Processing
Full transparency and control
Synheart Wellness Impact Protocol (SWIP)
HSI → A Single, Traceable Signal
SWIP merges emotion probabilities, focus features, and biosignal patterns into a unified score (0–100) that reflects your app's impact on human state in real time.
Key Inputs
HR / HRV / EDA / Motion (via Synheart Wear)
Emotion inference (via Synheart Emotion)
Focus load & behavioral movement signals
Output
SWIP Score — track emotional & cognitive impact
Signed, aggregate metrics — (safe for research & UX)
Emotion AI Must Protect Human State Itself
Synheart systems are engineered for human-state privacy:
Local First
No cloud emotional inference
Transparent Data Flow
Auditable, user-level controls
Signed Metrics
Integrity without surveillance
Open Science
Peer-validated research
"Understanding Emotion Should Never Come At The Cost Of Human Privacy."
From Signals to Empathy
Synheart technology bridges physiology, cognitive neuroscience, and affective computing. HSI expands beyond emotion — modeling focus, cognitive workload, and behavioral cues, enabling:
Emotion-aware apps
Focus-responsive interfaces
Human-state-aware AI agents
Ethical adaptive systems
"The Future of AI Is Not Artificial — It's Human-State Aware."
— Synheart Labs