CSI-based human activity recognition via lightweight compact convolutional transformers

Wong, Yan Chiew and Abuhoureyah, Fahd Saad Amed and Al-Taweel, Malik Hasan and Abdullah, Nihad Ibrahim (2024) CSI-based human activity recognition via lightweight compact convolutional transformers. Advances in Computational Design, 9 (3). pp. 187-211. ISSN 2383-8477

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Abstract

WiFi sensing integration enables non-intrusive and is utilized in applications like Human Activity Recognition (HAR) to leverage Multiple Input Multiple Output (MIMO) systems and Channel State Information (CSI) data for accurate signal monitoring in different fields, such as smart environments. The complexity of extracting relevant features from CSI data poses computational bottlenecks, hindering real-time recognition and limiting deployment on resource-constrained devices. The existing methods sacrifice accuracy for computational efficiency or vice versa, compromising the reliability of activity recognition within pervasive environments. The lightweight Compact Convolutional Transformer (CCT) algorithm proposed in this work offers a solution by streamlining the process of leveraging CSI data for activity recognition in such complex data. By leveraging the strengths of both CNNs and transformer models, the CCT algorithm achieves state-of-the-art accuracy on various benchmarks, emphasizing its excellence over traditional algorithms. The model matches convolutional networks’ computational efficiency with transformers’ modeling capabilities. The evaluation process of the proposed model utilizes self-collected dataset for CSI WiFi signals with few daily activities. The results demonstrate the improvement achieved by using CCT in real-time activity recognition, as well as the ability to operate on devices and networks with limited computational resources.

Item Type:	Article
Uncontrolled Keywords:	Recognition, Channel state information, Compact Convolutional Transformer, WiFi sensing
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	11 Aug 2025 04:52
Last Modified:	11 Aug 2025 04:52
URI:	http://eprints.utem.edu.my/id/eprint/28894
Statistic Details:	View Download Statistic

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