Sensor Fusion Techniques for Wearable Devices

Modern wearable devices combine multiple sensors to achieve accurate health monitoring while minimizing power consumption. This guide explores sensor fusion algorithms including Kalman filtering and complementary filters, with practical implementation guidance for combining IMU, PPG, and environmental sensor data.

• Extended Kalman Filter improves motion tracking accuracy by 40% over single-sensor approaches
• Multi-sensor PPG fusion reduces heart rate error to under 2 BPM during exercise
• Adaptive sampling reduces power consumption by 60% while maintaining accuracy
• Context-aware fusion algorithms improve activity recognition to 95% accuracy
• Edge processing of fused data reduces BLE transmission power by 70%

1. 01Introduction to Sensor Fusionp. 1
2. 02IMU Sensor Integrationp. 5
3. 03PPG and Heart Rate Monitoringp. 10
4. 04Kalman Filter Implementationp. 15
5. 05Power Optimization Strategiesp. 20
6. 06Real-World Implementation Examplesp. 25