Drowsiness prediction — minutes ahead of microsleep events.
Non invasive blood alcohol concentration (BAC) estimation.
Man down and overheating detection for lone or field workers.
Edge first data processing — reliable even offline.
Optional cloud analytics for dashboards and system learning.
How it’s built
Sensor‑agnostic architecture
Compatible with wearables, rPPG camera and RADAR sensors — all feeding the same diagnostic engine.
A unified AI pipeline for real-time physiological data analysis — consistent, robust, and reliable across multiple sensing technologies, either working standalone or in combination.
Edge only deployments or hybrid modes with anonymized metadata guarantee compliance with safety and privacy regulations.
Why it's different
Higher accuracy
SAT sets a new market benchmark for DMS by achieving greater than 93% accuracy with sensitivity values >93% and specificity >90% demonstrated
through extensive and accurate objective validation method
Higher reliability
Analysis based on physiological signals offers direct insight into the person’s behavioral state, maintaining accuracy where vision-based systems fail — when operators wear helmets, glasses, masks, or gloves, or in light changing conditions.
Fewer false positive
Reduced false alarms through physiological-based real-time analysis.
Flexible integration
Connects effortlessly with wired/wireless communication technology compatible with medical/health data exchange standards as well as the operational safety stack used by Environmental, Health & Safety (EHS) teams— ensuring interoperability across transport and industrial sectors.
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