Synheart builds privacy-preserving, human-state-aware AI from biosignals. We decode heart rate, HRV, EDA, and motion into real-time emotional, cognitive, and behavioral intelligence directly on your device.
Your physiological signals reveal patterns that are hard to notice.
Synheart's Human State Interface (HSI) interprets stress, focus, calm, and behavioral dynamics locally, ethically, and in real time, without sending sensitive data to the cloud.
Private, On-Device Processing
End-to-End State Inference
Local Inference Reliability
For decades, computers have listened to our words and tracked our behavior. Now they can understand the subtle rhythm beneath both, your heartbeat. Synheart's Human State Interface unifies:
Synheart is more than a model — it's an ecosystem connecting biosignal data, emotional inference, cognitive metrics, and interactive analytics.
Synheart is more than a model — it's an ecosystem connecting biosignal data, emotional inference, cognitive metrics, and interactive analytics.
Heart Rate
72 BPM
Emotional State
Stressed
SWIP score
88
Stress Time
56%
Focus Score
75
Behavior
360°
Every heartbeat tells a story — of stress, focus, calm, or joy.
Synheart interprets these signals locally and privately, enabling emotion-aware experiences without giving up privacy.
Privacy-first processing with zero cloud dependence.
HR, HRV, EDA, and motion combined for robust emotional and cognitive state estimation.
Benchmarked using peer-reviewed, public biosignal datasets.
Reliable accuracy for real-world emotion and focus applications.
Our work brings together affective computing, cognitive neuroscience, and biosignal modeling to understand how physiological patterns reflect human state.
We develop new HSI methods, open benchmarks, and collaborate with labs advancing emotion and cognition research.
"We are not thinking machines that feel; we are feeling machines that think."
Antonio Damasio
HRV
Our Research Spans Affective Computing, Cognitive Neuroscience, And AI. We Publish Open Datasets
Our pipeline transforms raw physiological data into clean, interpretable signals using advanced filtering, artifact removal, and feature extraction—all optimized for edge computing with minimal computational overhead.
Electrocardiogram and photoplethysmography signals for heart rate and cardiovascular monitoring
Variability metrics for stress, recovery, and autonomic state
Skin conductance signals reflecting arousal and emotional intensity
Motion and micro-movement data for context and behavior
Physiological thermal signals for state assessment
Whether You're A Researcher, Developer, Or Designer — If You Believe
Empathy Belongs In AI, There's A Place For You Here.
