CBR-ACE: Counting Human Exercise using Wi-Fi Beamforming Reports

• Item type: Journal(1)
• 公開日: 2022-01-15
• タイトル: CBR-ACE: Counting Human Exercise using Wi-Fi Beamforming Reports
• タイトル / 言語: en
• タイトル: CBR-ACE: Counting Human Exercise using Wi-Fi Beamforming Reports
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文] wireless LAN, remote sensing, channel state information, activity recognition
• 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 Information Science and Technology, Osaka University
• 著者所属: Access Network Service Systems Laboratory, NTT Corporation
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Access Network Service Systems Laboratory, NTT Corporation
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者名: Sorachi, Kato; Tomoki, Murakami; Takuya, Fujihashi; Takashi, Watanabe; Shunsuke, Saruwatari
• 著者名(英): Sorachi, Kato; Tomoki, Murakami; Takuya, Fujihashi; Takashi, Watanabe; Shunsuke, Saruwatari
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: As people spend more time indoors owing to the COVID-19 global pandemic, the automatic detection of indoor human activity has increasingly become of interest to researchers and consumers. Conventional Wi-Fi Channel State Information (CSI)-based detection provides adequate accuracy; however, they have a deployment constraint owing to specific hardware and software for full CSI acquisition. This study exploits the Compressed Beamforming Report (CBR), which is a default form of CSI in IEEE 802.11ac and 11ax, to address the constraint in Wi-Fi CSI-based methods. The CBRs are shared among most IEEE 802.11ac compliant devices and are easily obtained with outer sniffers. Our CBR-based Activity Count Estimator (CBR-ACE) is a novel wireless sensing system using CBRs. The CBR-ACE provides a Raspberry Pi-based tool to easily deploy a new wireless sensing system into existing networks, and utilizes the CBR irregularity for automatic detection. From experiments in real-dwelling environments, the proposed CBR-ACE achieves average estimation errors of 0.97 in the best case.; ------------------------------; 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.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.66; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: As people spend more time indoors owing to the COVID-19 global pandemic, the automatic detection of indoor human activity has increasingly become of interest to researchers and consumers. Conventional Wi-Fi Channel State Information (CSI)-based detection provides adequate accuracy; however, they have a deployment constraint owing to specific hardware and software for full CSI acquisition. This study exploits the Compressed Beamforming Report (CBR), which is a default form of CSI in IEEE 802.11ac and 11ax, to address the constraint in Wi-Fi CSI-based methods. The CBRs are shared among most IEEE 802.11ac compliant devices and are easily obtained with outer sniffers. Our CBR-based Activity Count Estimator (CBR-ACE) is a novel wireless sensing system using CBRs. The CBR-ACE provides a Raspberry Pi-based tool to easily deploy a new wireless sensing system into existing networks, and utilizes the CBR irregularity for automatic detection. From experiments in real-dwelling environments, the proposed CBR-ACE achieves average estimation errors of 0.97 in the best case.; ------------------------------; 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.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.66; ------------------------------
• 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 63, 号 1, 発行日 2022-01-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764