Challenge
A remote patient monitoring wearable required real-time arrhythmia detection directly on the device,
without uploading sensitive ECG data to the cloud. The system needed to ensure high accuracy,
ultra-low power consumption, and complete data privacy.
Solution
- On-Device AI Model: TFLite Micro model trained on PhysioNet ECG dataset and synthetic variants.
- Lightweight Optimization: Model compressed to 32KB using INT8 quantization, enabling fast inference.
- Real-Time Processing: Inference completed in under 50ms on ARM Cortex-M4.
- Edge Anomaly Detection: Irregular heartbeats trigger instant local alerts and notifications.
- Privacy-First Cloud Design: Only event timestamps and risk scores are transmitted, not raw ECG data.
Outcome
- High Sensitivity: 98% accuracy in detecting true arrhythmias.
- High Specificity: 96% accuracy with minimal false positives.
- Low Power Consumption: Under 2mA average, enabling 14-day battery life.
- Enhanced Privacy: Zero raw ECG data leaves the device.
- Scalable Deployment: 50,000+ devices successfully deployed in real-world environments.
