A practical wearable fall detection system based on tiny convolutional neural networks

Abstract

Falls are a major public health problem in a rapidly aging society due to their high prevalence and severe consequences among the older population. Therefore, automatic fall detection is of primary importance to enable timely medical intervention for older fall victims. The existing fall detection methods suffer from either poor robustness or high computational cost, limiting their practical applications in wearable fall detection systems. In this study, a tiny convolutional neural network (TinyCNN) with two-stage efficient feature extraction was proposed and evaluated on two large-scale public fall datasets (KFall and SisFall) collected from wearable inertial sensors. Cross-validation results showed that TinyCNN achieved both mean sensitivities and specificities over 99 % on the KFall, and over 98 % on the SisFall. While benefiting from the effectiveness of TinyCNN, we also addressed its black-box effect using class activation map (CAM). Furthermore, by employing quantization techniques, TinyCNN achieved a low latency of 0.037 s on an ultra-low-power microcontroller (Arduino Nano BLE 33 Sense), enabling real-time performance without sacrificing the accuracy. Compared with existing state-ofthe-art fall detection algorithms, the newly proposed TinyCNN yielded a more balanced performance in terms of accuracy and practicality. Inspired by these promising results, we further developed a practical wearable prototype system based on this embeddable TinyCNN and an accompanying mobile App for automatic fall detection and immediate alarm. This AI-enabled wearable system has great potential for real-life fall detection applications among older individuals.