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Wearables

Sensor Fusion Techniques for Wearable Devices

Combining IMU, PPG, and Environmental Sensors for Accurate Health Monitoring

Technical summary
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Executive summary

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.

Key findings

  • Extended Kalman filtering improves motion tracking over single-sensor approaches by combining IMU axes and correcting drift
  • Fusing PPG with accelerometer data reduces motion artefacts in heart-rate estimates during exercise
  • Adaptive sampling reduces sensor power by sampling fast only when activity demands it
  • Context-aware fusion improves activity recognition compared with single-sensor classifiers
  • Edge processing of fused data reduces the data sent over BLE, and with it radio energy, by up to an order of magnitude

What the paper covers

  1. 01Introduction to Sensor Fusion
  2. 02IMU Sensor Integration
  3. 03PPG and Heart Rate Monitoring
  4. 04Kalman Filter Implementation
  5. 05Power Optimization Strategies
  6. 06Real-World Implementation Examples
“Sensor fusion is the process of combining data from multiple sensors to produce more accurate, reliable, and comprehensive information than any single sensor could provide.”
Rapid Circuitry engineering team, Wearable Device Specialists

Related topics

  • sensor fusion
  • wearables
  • IMU
  • PPG
  • health monitoring
  • Kalman filter