Lightweight Convolutional Neural Network-Based Computer Vision Model for Human Behavior Analysis on Consumer Internet of Things Devices

Abstract

The rapid advancements of technology have encouraged the growth of the Internet of Things (IoT), which has transformed how individuals interact with their environments. Among its many branches, Consumer IoT (CIoT) has emerged as a leading force by integrating IoT elements into everyday devices, enhancing user experiences, and offering intelligent services. In particular, smart home environments powered by CIoT devices are improving the quality of life, specifically for the elderly and individuals with disabilities, through automation and behaviour monitoring. To efficiently analyze human behaviour in such settings, this study proposes a novel lightweight computer vision technique, LCNNCV-HBA (Lightweight Convolutional Neural Network-Based Computer Vision for Human Behavior Analysis), specifically optimized for resource-constrained CIoT devices. The proposed method begins with Median Filtering (MF) to eliminate noise, followed by ConvNeXtTiny, a compact yet effective deep learning architecture used for feature extraction by capturing key spatial patterns from images with minimal resource consumption. For behaviour classification, a stacked denoising autoencoder (SDAE) is employed, while an Improved Sparrow Search Algorithm (ISSA) is used to fine-tune hyperparameters and enhance model performance. Experimental validation conducted on a benchmark image dataset demonstrates the effectiveness of the proposed LCNNCV-HBA approach, achieving a superior accuracy of 98.56%, outperforming existing methods in both efficiency and precision.