Skeleton-Based Human Action Recognition Using Multitype Input Data on Edge Devices in Internet of Things Systems

The growing demand for smart surveillance systems, particularly in elderly and patient care, has driven significant advancements in human action recognition (HAR) technologies. Despite these advancements, real-time HAR deployment in Internet of Things (IoT) environments remains limited by latency, bandwidth, and the tradeoff between model size and performance. To address these limitations, we propose Tiny-HAR, a lightweight HAR framework tailored to edge devices. Built on DSConVBlock_N, Tiny-HAR comprises two key modules: a high-level feature extraction module and a temporal deep feature extraction module. Using skeletal data and diverse spatiotemporal information, Tiny-HAR achieves state-of-the-art accuracy while remaining compact. Experimental analysis indicates its high performance across various benchmark datasets: 82.1% on joint-annotated human motion database (JHMDB); 96.7% and 93.2% on SHREC2017 for 14 and 28 classes, respectively; 90.27% on MPOSE2021; and 97.62% on University of Rzeszów (UR) Falling. Tiny-HAR also achieves up to nine times faster inference and 11.5 times fewer giga floating-point operations (GFLOPs) compared with existing models. Deployment on a Raspberry Pi 4 Model B confirmed its real-time capability with an inference time of 2.8 ms. Overall, these results position Tiny-HAR as a promising solution for efficient and reliable HAR in IoT-based health monitoring applications.