Document Type : Original/Review Paper

Authors

• Fatemeh Naghavi ¹
• Kourosh Kiani ²

¹ Electrical and Computer Engineering Department, Semnan University

² Electrical and Computer Engineering, Semnan University, Iran

Abstract

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.

Keywords

• Human Activity Recognition
• Elderly Daily Activity
• Monitoring daily activities
• CNN
• Spatio_Temporal Features

Main Subjects

• H.3.2.2. Computer vision