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A multi-scale time segments attention mechanism for sensor-based human activity recognition

Rong Hailong, Wang Hao, Wu Xiaohui, Jin Tianlei, Zou Ling

Metrology and Measurement Systems

2025

Vol. 32, nr 1

1--17

Sensor-based Human Activity Recognition (SHAR) technology is dedicated to utilizing sensor signals from smart devices to detect and identify human activities, thereby assisting in daily life. With the successful application of deep learning techniques, researchers are exploring the potential of integrating them with SHAR. Traditional fixed sliding window methods for processing datasets often lead to multi-class activity mixing. To alleviate this issue, researchers have introduced time attention mechanisms to focus on key temporal points related to activities. To address this challenge, we propose an innovative Multi-scale Time Segments Attention Mechanism (MTSA), which diverges from traditional time attention mechanisms by focusing on time segments pertinent to activities, better aligning with the characteristics of SHAR data and significantly reducing computational resource consumption. Our experiments on recognized datasets such as UCI-HAR, PAMAP2, and WISDM validate the effectiveness of MTSA, demonstrating that it can be seamlessly integrated into existing SHAR models, enhancing performance without adding extra computational overhead.