计算机科学
深度学习
活动识别
人工智能
模式识别(心理学)
嵌入式系统
作者
Xingcan Chen,Yi Bo Zou,C. Li,Wendong Xiao
出处
期刊:IEEE Transactions on Human-Machine Systems
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 68-78
被引量:1
标识
DOI:10.1109/thms.2023.3348694
摘要
Human activity recognition (HAR) is a key technology in the field of human–computer interaction. Unlike systems using sensors or special devices, the WiFi channel state information (CSI)-based HAR systems are noncontact and low cost, but they are limited by high computational complexity and poor cross-domain generalization performance. In order to address the above problems, a reconstructed WiFi CSI tensor and deep learning based lightweight HAR system (Wisor-DL) is proposed, which firstly reconstructs WiFi CSI signals with a sparse signal representation algorithm, and a CSI tensor construction and decomposition algorithm. Then, gated temporal convolutional network with residual connections is designed to enhance and fuse the features of the reconstructed WiFi CSI signals. Finally, dendrite network makes the final decision of activity instead of the traditional dense layer. Experimental results show that Wisor-DL is a lightweight HAR system with high recognition accuracy and satisfactory cross-domain generalization ability.
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