AI-Enabled IoT Wearable System for Real-Time Fall Detection and Emergency Alerting in Older Adults
TL;DR
An edge-based wearable IoT framework for real-time fall detection based on the accelerometer and gyroscope signals provides a good base for the private, rapid fall detection but still needs to be validated in the real world, tested with hardware, put through with a variety of elderly participants and evaluated for usability.
Abstract
Falls are a significant health issue for older people, and can result in injury, disability, hospitalisation and loss of independence. Continuous monitoring and quick caregiver notification can be enabled using an AI-powered wearable IoT system. Objective: The purpose of this study was to design and test an edge-based wearable IoT framework for real-time fall detection based on the accelerometer and gyroscope signals. Methodology: A synthetic dataset was created by using the features of SisFall and MobiAct, including 270 falls and 630 activities of daily living from 30 simulated participants. Various temporal, statistical and frequency domain features were extracted from the sensor signals and these were filtered, normalized, segmented. Participant-wise Training and Testing were performed on the Random Forest and Support Vector Machine Models. The architecture of ESP32–IMU and the alert mechanism using MQTT was designed. On the synthetic test data, both models obtained a 100% accuracy, sensitivity, specificity, precision and F1-score, while the AUC is 1.000 for both models. Discussion: The results show technical feasibility, and probably represent good synthetic class separation, and should not be used as a measure of clinical performance. Discussion: The framework provides a good base for the private, rapid fall detection but still needs to be validated in the real world, tested with hardware, put through with a variety of elderly participants and evaluated for usability.