Human Activity Recognition Using FixMatch-based Semi-supervised Learning with CSI

• Item type: Journal(1)
• 公開日: 2024-08-15
• タイトル: Human Activity Recognition Using FixMatch-based Semi-supervised Learning with CSI
• タイトル / 言語: en
• タイトル: Human Activity Recognition Using FixMatch-based Semi-supervised Learning with CSI
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:“Applications and the Internet” in Conjunction with the Main Topics of COMPSAC 2023] channel state information, AI, IoT, Wi-Fi, semi-supervised learning
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Integrated Science and Technology, Shizuoka University
• 著者所属: NTT DOCOMO, INC.
• 著者所属: NTT DOCOMO, INC.
• 著者所属: NTT DOCOMO, INC.
• 著者所属: Graduate School of Integrated Science and Technology, Shizuoka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Integrated Science and Technology, Shizuoka University
• 著者所属(英): en
• 著者所属(英): NTT DOCOMO, INC.
• 著者所属(英): en
• 著者所属(英): NTT DOCOMO, INC.
• 著者所属(英): en
• 著者所属(英): NTT DOCOMO, INC.
• 著者所属(英): en
• 著者所属(英): Graduate School of Integrated Science and Technology, Shizuoka University
• 著者名: Kyosuke, Teramoto; Tomoki, Haruyama; Takuru, Shimoyama; Fumihiko, Kato; Hiroshi, Mineno
• 著者名(英): Kyosuke, Teramoto; Tomoki, Haruyama; Takuru, Shimoyama; Fumihiko, Kato; Hiroshi, Mineno
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: With the recent development of smart homes and IoT, sensing of daily activities has been attracting attention. Wi-Fi CSI-based methods have been studied as a method for activity recognition because they can reduce costs and protect privacy by not showing the identity of people. In a previous study it was reported that CSI can recognize simple human motions indoors. However, in the previous studies, the CSI-based methods were not able to recognize the various activities of people in their daily lives sufficiently. In addition, the training data is often manually annotated, and the training cost is high. Therefore, in this study, we performed action recognition using semi-supervised learning with a 1D-CNN on three receivers for everyday actions. The proposed method uses a 1D-CNN model to identify which room the subject is in when identifying actions such as global learning and then uses semi-supervised learning based on the FixMatch method as local learning to recognize more detailed actions. Although FixMatch was originally designed for image classification, the proposed method is implemented in such a way that it is effective even for time-series data format by using a data expansion method for time-series data. In basic experiments, we confirmed that the proposed method achieves an accuracy of 98% for global learning to determine rooms, and an accuracy higher than that of conventional supervised learning for local learning to recognize detailed actions.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.32(2024) (online); DOI http://dx.doi.org/10.2197/ipsjjip.32.596; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: With the recent development of smart homes and IoT, sensing of daily activities has been attracting attention. Wi-Fi CSI-based methods have been studied as a method for activity recognition because they can reduce costs and protect privacy by not showing the identity of people. In a previous study it was reported that CSI can recognize simple human motions indoors. However, in the previous studies, the CSI-based methods were not able to recognize the various activities of people in their daily lives sufficiently. In addition, the training data is often manually annotated, and the training cost is high. Therefore, in this study, we performed action recognition using semi-supervised learning with a 1D-CNN on three receivers for everyday actions. The proposed method uses a 1D-CNN model to identify which room the subject is in when identifying actions such as global learning and then uses semi-supervised learning based on the FixMatch method as local learning to recognize more detailed actions. Although FixMatch was originally designed for image classification, the proposed method is implemented in such a way that it is effective even for time-series data format by using a data expansion method for time-series data. In basic experiments, we confirmed that the proposed method achieves an accuracy of 98% for global learning to determine rooms, and an accuracy higher than that of conventional supervised learning for local learning to recognize detailed actions.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.32(2024) (online); DOI http://dx.doi.org/10.2197/ipsjjip.32.596; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 65, 号 8, 発行日 2024-08-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764
• 公開者 / 言語: ja
• 公開者 / 出版者: 情報処理学会