Efficient fall detection using lightweight network to enhance smart internet of things

Abstract

Fall detection automatically recognizes human falls, mainly to monitor and prevent severe injury and potential fatalities. It can be developed by applying deep learning methods to recognize human subjects during fall incidents and implemented in the internet of things (IoT) to monitor patient and elderly individuals’ activity. The development of object detection presents you only look once v8 (YOLOv8) as an influential network, but its efficiency needs to be improved. A modified YOLOv8 architecture is proposed to introduce a novel lightweight network version called YOLOv8-Hypernano (YOLOv8h) that recognizes fall events. The backbone incorporates a combined spatial and channel attention module, which enhances focus on human subjects by concentrating on movement patterns to detect falls more accurately. This work also offers a consecutive selective enhancement (CSE) module to improve efficiency and effectiveness in feature extraction while reducing computational costs. The neck structure is modified by adding a lightweight bottleneck network. The proposed network reconstructs feature maps in depth, paying more attention to accurate human movement patterns and enhancing efficiency and effectiveness in feature extraction. Experimental results of YOLOv8h with the light bottleneck and consecutive selective enhancement modules show giga floating-point operations per seconds (GFLOPS) of 5.6 and 1,194,440 parameters. The model performance is calculated in mean average precision, achieving 0.603 and 0.732 on the Le2i and Fallen datasets, respectively. These results demonstrate that the optimized network improves accuracy performance while maintaining lightweight computing requirements that can run smoothly on IoT devices, achieving comparable speed and efficiency suitable for operation on low-cost computing devices.