Energy-Efficient TinyML-Based Fall Detection for Wearable Healthcare Devices

Abstract

Falls and irregular heart rhythms are the main causes of injury among kids and the elderly, always overwhelming healthcare systems, and thus making privacy-aware, real-time monitoring a necessity. This work unveils a TinyML wrist-worn prototype based on Arduino Nano 33 BLE Sense, which combines the MPU6050 IMU for motion-based fall detection and activity (walking, sitting, running, lying) recognition with the MAX30102 PPG for heartbeat, SpO2, and HRV anomaly detection over generations. The device, tested on 7 subjects (3 children 8-12 years, 2 adults 25-40, and 2 seniors 65-75) for 140 real-life sequences in a lab in Kerala, uses Butterworth-filtered data, 56 temporal features extracted from 256-sample windows, and the optimized hybrid CNN-LSTM model (65% structured pruning, 8-bit QAT) to perform inference on the edge under 217KB flash. Dual-threshold triggering (fall confidence >0.9 plus HR anomalies or SpO2<92%) allows BLE alerts within 100ms to caregiver apps, and cancellation via 30s haptic/button helps reduce the false alarms. Field experiments demonstrated the device performance with 94.3% accuracy, 0.95 fall F1-score, 38ms latency, 0.7mW power, and 2.1% false positives, showing a significant improvement of 15% F1 when compared against unimodal baselines, while being fully processed on the edge, GDPR-compliant, and with a multi-day battery life, the device is ready for wide deployment in homes, schools, and care facilities. This work is a step forward in TinyML across demographics, thus opening the gate to multimodal extensions such as cry detection.