Transfer-Learning-Based Human Activity Recognition Using Antenna Array
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 siz...
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Published in | Remote sensing (Basel, Switzerland) Vol. 16; no. 5; p. 845 |
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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. |
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AbstractList | 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. |
Audience | Academic |
Author | Zhou, Lang Zheng, Zheng Cai, Yongbin Zhang, Xuebo Xiao, Lijun Lin, Jiaqing Ye, Kun Wu, Sheng |
Author_xml | – sequence: 1 givenname: Kun surname: Ye fullname: Ye, Kun – sequence: 2 givenname: Sheng surname: Wu fullname: Wu, Sheng – sequence: 3 givenname: Yongbin surname: Cai fullname: Cai, Yongbin – sequence: 4 givenname: Lang surname: Zhou fullname: Zhou, Lang – sequence: 5 givenname: Lijun surname: Xiao fullname: Xiao, Lijun – sequence: 6 givenname: Xuebo orcidid: 0000-0001-5164-754X surname: Zhang fullname: Zhang, Xuebo – sequence: 7 givenname: Zheng surname: Zheng fullname: Zheng, Zheng – sequence: 8 givenname: Jiaqing surname: Lin fullname: Lin, Jiaqing |
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SubjectTerms | Accuracy activity recognition Antenna arrays Antennas Antennas (Electronics) Artificial neural networks CSI Fourier transforms Human activity recognition Learning Low pass filters Network interface cards Neural networks Noise control Noise reduction Noise threshold Privacy, Right of Receivers & amplifiers ResNet18 Semiconductor industry Training Transfer learning Transmitters Wi-Fi WiFi sensing |
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Title | Transfer-Learning-Based Human Activity Recognition Using Antenna Array |
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