{"title":"用于口腔活动识别的传感器嵌入牙齿","authors":"Cheng-Yuan Li, Yen-Chang Chen, Wei-Ju Chen, Polly Huang, Hao-Hua Chu","doi":"10.1145/2493988.2494352","DOIUrl":null,"url":null,"abstract":"This paper presents the design and implementation of a wearable oral sensory system that recognizes human oral activities, such as chewing, drinking, speaking, and coughing. We conducted an evaluation of this oral sensory system in a laboratory experiment involving 8 participants. The results show 93.8% oral activity recognition accuracy when using a person-dependent classifier and 59.8% accuracy when using a person-independent classifier.","PeriodicalId":90988,"journal":{"name":"The semantic Web--ISWC ... : ... International Semantic Web Conference ... proceedings. International Semantic Web Conference","volume":"7 1","pages":"41-44"},"PeriodicalIF":0.0000,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"47","resultStr":"{\"title\":\"Sensor-embedded teeth for oral activity recognition\",\"authors\":\"Cheng-Yuan Li, Yen-Chang Chen, Wei-Ju Chen, Polly Huang, Hao-Hua Chu\",\"doi\":\"10.1145/2493988.2494352\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the design and implementation of a wearable oral sensory system that recognizes human oral activities, such as chewing, drinking, speaking, and coughing. We conducted an evaluation of this oral sensory system in a laboratory experiment involving 8 participants. The results show 93.8% oral activity recognition accuracy when using a person-dependent classifier and 59.8% accuracy when using a person-independent classifier.\",\"PeriodicalId\":90988,\"journal\":{\"name\":\"The semantic Web--ISWC ... : ... International Semantic Web Conference ... proceedings. International Semantic Web Conference\",\"volume\":\"7 1\",\"pages\":\"41-44\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"47\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The semantic Web--ISWC ... : ... International Semantic Web Conference ... proceedings. International Semantic Web Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2493988.2494352\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The semantic Web--ISWC ... : ... International Semantic Web Conference ... proceedings. International Semantic Web Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2493988.2494352","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sensor-embedded teeth for oral activity recognition
This paper presents the design and implementation of a wearable oral sensory system that recognizes human oral activities, such as chewing, drinking, speaking, and coughing. We conducted an evaluation of this oral sensory system in a laboratory experiment involving 8 participants. The results show 93.8% oral activity recognition accuracy when using a person-dependent classifier and 59.8% accuracy when using a person-independent classifier.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
