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  1. 論文誌(ジャーナル)
  2. Vol.63
  3. No.1

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

https://ipsj.ixsq.nii.ac.jp/records/215834
https://ipsj.ixsq.nii.ac.jp/records/215834
4a809952-9b66-4e98-bd80-8776c72a0fd0
名前 / ファイル ライセンス アクション
IPSJ-JNL6301024.pdf IPSJ-JNL6301024.pdf (11.4 MB)
Copyright (c) 2022 by the Information Processing Society of Japan
オープンアクセス
Item type Journal(1)
公開日 2022-01-15
タイトル
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
言語 eng
キーワード
主題Scheme Other
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
Graduate School of Information Science and Technology, Osaka University
著者所属(英)
en
Access Network Service Systems Laboratory, NTT Corporation
著者所属(英)
en
Graduate School of Information Science and Technology, Osaka University
著者所属(英)
en
Graduate School of Information Science and Technology, Osaka University
著者所属(英)
en
Graduate School of Information Science and Technology, Osaka University
著者名 Sorachi, Kato

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Sorachi, Kato

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Tomoki, Murakami

× Tomoki, Murakami

Tomoki, Murakami

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Takuya, Fujihashi

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Takuya, Fujihashi

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Takashi, Watanabe

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Takashi, Watanabe

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Shunsuke, Saruwatari

× Shunsuke, Saruwatari

Shunsuke, Saruwatari

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著者名(英) Sorachi, Kato

× Sorachi, Kato

en Sorachi, Kato

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Tomoki, Murakami

× Tomoki, Murakami

en Tomoki, Murakami

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Takuya, Fujihashi

× Takuya, Fujihashi

en Takuya, Fujihashi

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Takashi, Watanabe

× Takashi, Watanabe

en Takashi, Watanabe

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Shunsuke, Saruwatari

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en Shunsuke, Saruwatari

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論文抄録
内容記述タイプ Other
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
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
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
収録物識別子 1882-7764
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