論文

査読有り
2015年

Brain-machine interfaces for assistive smart homes: A feasibility study with wearable near-infrared spectroscopy

2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
  • Takeshi Ogawa
  • ,
  • Jun-ichiro Hirayama
  • ,
  • Pankaj Gupta
  • ,
  • Hiroki Moriya
  • ,
  • Shumpei Yamaguchi
  • ,
  • Akihiro Ishikawa
  • ,
  • Yoshihiro Inoue
  • ,
  • Motoaki Kawanabe
  • ,
  • Shin Ishii

開始ページ
1107
終了ページ
1110
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/EMBC.2015.7318559
出版者・発行元
IEEE

Smart houses for elderly or physically challenged people need a method to understand residents' intentions during their daily-living behaviors. To explore a new possibility, we here developed a novel brain-machine interface (BMI) system integrated with an experimental smart house, based on a prototype of a wearable near-infrared spectroscopy (NIRS) device, and verified the system in a specific task of controlling of the house's equipments with BMI. We recorded NIRS signals of three participants during typical daily-living actions (DLAs), and classified them by linear support vector machine. In our off-line analysis, four DLAs were classified at about 70% mean accuracy, significantly above the chance level of 25%, in every participant. In an online demonstration in the real smart house, one participant successfully controlled three target appliances by BMI at 81.3% accuracy. Thus we successfully demonstrated the feasibility of using NIRS-BMI in real smart houses, which will possibly enhance new assistive smart-home technologies.

Web of Science ® 被引用回数 : 6

リンク情報
DOI
https://doi.org/10.1109/EMBC.2015.7318559
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000371717201100&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/conf/embc/embc2015.html#conf/embc/OgawaHGMYIIKI15
ID情報
  • DOI : 10.1109/EMBC.2015.7318559
  • ISSN : 1557-170X
  • DBLP ID : conf/embc/OgawaHGMYIIKI15
  • Web of Science ID : WOS:000371717201100

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