Shahrood University of Technology Journal of AI and Data Mining 2322-5211 Articles in Press 2026 07 04 Skeleton based Human Action Recognition for Monitoring Elderly People 3836 10.22044/jadm.2026.16786.2810 EN Fatemeh Naghavi Electrical and Computer Engineering Department, Semnan University Kourosh Kiani Electrical and Computer Engineering, Semnan University, Iran Journal Article 2025 09 06 Monitoring the daily activities of elderly individuals plays a crucial role in accident prevention, health assessment, and improving quality of life. In this paper, we propose a lightweight and efficient convolutional neural network architecture for human activity recognition based on skeletal data. Unlike conventional approaches that rely solely on absolute joint coordinates, the proposed method incorporates short- and long-term frame differences as well as spatial variations across joints to construct complementary views, thereby providing a richer spatiotemporal representation. The architecture consists of multiple convolutional blocks with residual connections, followed by global average pooling and a fully connected layer for final classification. Experimental evaluations conducted on two benchmark datasets, NTU RGB+D and ETRI-Activity3D, demonstrate that while the proposed model may achieve slightly lower accuracy compared to some state-of-the-art methods, it offers high inference speed and low computational complexity. These characteristics make it particularly suitable for real-time applications and deployment on resource-constrained devices, especially in elderly home-care environments.