1. Transfer-Learning-Based Human Activity Recognition Using Antenna Array.
- Author
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Ye, Kun, Wu, Sheng, Cai, Yongbin, Zhou, Lang, Xiao, Lijun, Zhang, Xuebo, Zheng, Zheng, and Lin, Jiaqing
- Subjects
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HUMAN activity recognition , *ANTENNA arrays , *CONVOLUTIONAL neural networks , *ARRAY processing - Abstract
Due to its low cost and privacy protection, Channel-State-Information (CSI)-based activity detection has gained interest recently. However, to achieve high accuracy, which is challenging in practice, a significant number of training samples are required. To address the issues of the small sample size and cross-scenario in neural network training, this paper proposes a WiFi human activity-recognition system based on transfer learning using an antenna array: Wi-AR. First, the Intel5300 network card collects CSI signal measurements through an antenna array and processes them with a low-pass filter to reduce noise. Then, a threshold-based sliding window method is applied to extract the signal of independent activities, which is further transformed into time–frequency diagrams. Finally, the produced diagrams are used as input to a pretrained ResNet18 to recognize human activities. The proposed Wi-AR was evaluated using a dataset collected in three different room layouts. The testing results showed that the suggested Wi-AR recognizes human activities with a consistent accuracy of about 94%, outperforming the other conventional convolutional neural network approach. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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