Automatic Eating Stage Classification using ASMR videos

• Item type: SIG Technical Reports(1)
• 公開日: 2022-05-30
• タイトル: Automatic Eating Stage Classification using ASMR videos
• タイトル / 言語: en
• タイトル: Automatic Eating Stage Classification using ASMR videos
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 感覚・知覚
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Faculty of Policy Management, Keio University
• 著者所属: Graduate School of Media and Governance, Keio University
• 著者所属: Faculty of Environment and Information Studies, Keio University
• 著者所属: Faculty of Environment and Information Studies, Keio University
• 著者所属(英): en
• 著者所属(英): Faculty of Policy Management, Keio University
• 著者所属(英): en
• 著者所属(英): Graduate School of Media and Governance, Keio University
• 著者所属(英): en
• 著者所属(英): Faculty of Environment and Information Studies, Keio University
• 著者所属(英): en
• 著者所属(英): Faculty of Environment and Information Studies, Keio University
• 著者名: Mari, Izumikawa; Takafumi, Kawasaki; Tadashi, Okoshi; Jin, Nakazawa
• 著者名(英): Mari, Izumikawa; Takafumi, Kawasaki; Tadashi, Okoshi; Jin, Nakazawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: A balanced diet and an appropriate calorie intake are the keys to both preventing and treating type II diabetes. Meanwhile, widespread techniques such as manual food logs and food image captures have been posing burdens on those with diabetes and have made diet monitoring difficult to become part of one's routine. The ultimate aim of this study is to develop an earable device that monitors a volume of food intake automatically. However, an automatic food intake volume monitoring requires a detection of biting, chewing, and swallowing sounds with foods of various sizes and textures. The present research therefore attempted to classify an eating sound, collected from YouTube eating ASMR, into one of the following labels: bite/chew, swallow, or other. A CNN machine learning model using sound features as input achieved an accuracy of 86%.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: A balanced diet and an appropriate calorie intake are the keys to both preventing and treating type II diabetes. Meanwhile, widespread techniques such as manual food logs and food image captures have been posing burdens on those with diabetes and have made diet monitoring difficult to become part of one's routine. The ultimate aim of this study is to develop an earable device that monitors a volume of food intake automatically. However, an automatic food intake volume monitoring requires a detection of biting, chewing, and swallowing sounds with foods of various sizes and textures. The present research therefore attempted to classify an eating sound, collected from YouTube eating ASMR, into one of the following labels: bite/chew, swallow, or other. A CNN machine learning model using sound features as input achieved an accuracy of 86%.
• 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: AA11838947
• 書誌情報: 研究報告ユビキタスコンピューティングシステム（UBI）; 巻 2022-UBI-74, 号 9, p. 1-7, 発行日 2022-05-30
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8698
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会