Wearable IoT fall detection systems combine IMU sensors (accelerometers and gyroscopes), detection algorithms, and IoT connectivity to automatically identify falls and alert caregivers within 30 seconds. The pipeline covers hardware (MPU-6050, 6-axis IMU), algorithm approaches ranging from threshold-based FSMs to CNN-LSTM deep learning models, and communication protocols (NB-IoT, BLE, LoRaWAN). Key challenges include the lab-vs-real-world accuracy gap, false positives causing user disengagement, and privacy concerns. Consumer devices like Apple Watch Series 4+ and Samsung Galaxy Watch implement multi-sensor confirmation windows, while dedicated medical pendants offer 24/7 monitoring for high-risk users. Emerging techniques like federated learning and Wi-Fi CSI analysis are addressing privacy and consistency gaps.
