WEKO3
アイテム
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/2158344a809952-9b66-4e98-bd80-8776c72a0fd0
名前 / ファイル | ライセンス | アクション |
---|---|---|
![]() |
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
× Sorachi, Kato
× Tomoki, Murakami
× Takuya, Fujihashi
× Takashi, Watanabe
× Shunsuke, Saruwatari
|
|||||||||||||||
著者名(英) |
Sorachi, Kato
× Sorachi, Kato
× Tomoki, Murakami
× Takuya, Fujihashi
× Takashi, Watanabe
× Shunsuke, Saruwatari
|
|||||||||||||||
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 |