Smarter Eyewear — Using Commercial EOG Glasses for Activity Recognition
| 言語 | 英語 |
|---|---|
| 種類 | 国際会議等の予稿 |
| 著者 | Shoya Ishimaru，Kai Kunze，Katsuma Tanaka，Yuji Uema，Koichi Kise，Masahiko Inami |
| 論文名 | Smarter Eyewear — Using Commercial EOG Glasses for Activity Recognition |
| 論文誌名 | Proceedings of the 2014 ACM Conference on pervasive and ubiquitous computing adjunct publication (UbiComp2014) |
| ページ | pp.239-242 |
| 発表場所 | Seattle, USA |
| 査読の有無 | 有 |
| 発表の種類 | 口頭ならびにポスター発表 |
| 年月 | 2014年9月 |
| 分野 | パターン認識 |
| DOI | 10.1145/2638728.2638795 |
| キー | Ishimaru2014_UbiComp_Smarter (id=1421) |
要約
Smart eyewear computing is a relatively new subcategory in ubiquitous computing research, which has enormous potential. In this paper we present a first evaluation of soon commercially available Electrooculography (EOG) glasses (J!NS MEME) for the use in activity recognition. We discuss the potential of EOG glasses and other smart eye-wear. Afterwards, we show a first signal level assessment of MEME, and present a classification task using the glasses. We are able to distinguish of 4 activities for 2 users ( typing, reading, eating and talking) using the sensor data (EOG and acceleration) from the glasses with an accuracy of 70 % for 6 sec. windows and up to 100 % for a 1 minute majority decision. The classification is done user-independent.
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