Real-time fatigue prediction for the advanced
manufacturing supply chain.
Three interconnected subsystems forming a closed-loop fatigue intelligence pipeline — from raw signal to actionable prediction.
Privacy-First Pose Estimation
Our proprietary lightweight YOLOv8 models process CCTV feeds locally. We extract skeletal metadata (gait, posture) without ever identifying the individual. No Facial Recognition. GDPR-compliant by design.
Signal Denoising Engine
Raw PPG and accelerometer data is processed on-device (Edge AI). Our filters remove industrial noise (vibration, heavy lifting) to isolate true fatigue markers in real-time.
Multi-Modal Probabilistic AI
Synthesizing vision and biometric streams. The engine calculates a 'Resilience Score' for every shift, predicting failure points 30 minutes before they occur.
Three pillars powering next-generation workforce safety analytics across high-risk manufacturing environments.
All inference happens on-device at the point of capture. Zero data leaves the factory floor. Sub-10ms latency for real-time alerting with our custom silicon-optimized runtime.
Fusing CCTV video streams with wearable biometric data in a unified temporal model. Cross-modal attention networks extract fatigue signatures invisible to any single sensor.
GDPR-compliant by architecture. Skeletal pose estimation strips all personally identifiable features before processing. No faces, no biometrics stored — only anonymised motion vectors.