Takuma Hashimoto, Suzanne Low, K. Fujita, Risa Usumi, Hiroshi Yanagihara, Chihiro Takahashi, M. Sugimoto, Yuta Sugiura
{"title":"舌输入:通过舌手势输入的方法,使用嵌入舌套的光学传感器","authors":"Takuma Hashimoto, Suzanne Low, K. Fujita, Risa Usumi, Hiroshi Yanagihara, Chihiro Takahashi, M. Sugimoto, Yuta Sugiura","doi":"10.23919/SICE.2018.8492690","DOIUrl":null,"url":null,"abstract":"We proposed a system to recognize tongue gestures by mounting a mouthpiece embedded with an array of photo-reflective sensors, to measure the changes in distance between the tongue surface and the back of the upper teeth when the tongue moves. The system utilizes grayscale images of the sensor values to calculate the HOG feature descriptor and to use SVM to recognize the gesture. We conducted two experiments to evaluate the accuracy of the system to estimate 4 tongue positions and 4 tongue gestures, where we obtained a recognition rate of 85.67% for positions and 77.5% for gestures. However, we observed that we can improve the rate by improving the issues we discovered.","PeriodicalId":425164,"journal":{"name":"2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"TongueInput: Input Method by Tongue Gestures Using Optical Sensors Embedded in Mouthpiece\",\"authors\":\"Takuma Hashimoto, Suzanne Low, K. Fujita, Risa Usumi, Hiroshi Yanagihara, Chihiro Takahashi, M. Sugimoto, Yuta Sugiura\",\"doi\":\"10.23919/SICE.2018.8492690\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We proposed a system to recognize tongue gestures by mounting a mouthpiece embedded with an array of photo-reflective sensors, to measure the changes in distance between the tongue surface and the back of the upper teeth when the tongue moves. The system utilizes grayscale images of the sensor values to calculate the HOG feature descriptor and to use SVM to recognize the gesture. We conducted two experiments to evaluate the accuracy of the system to estimate 4 tongue positions and 4 tongue gestures, where we obtained a recognition rate of 85.67% for positions and 77.5% for gestures. However, we observed that we can improve the rate by improving the issues we discovered.\",\"PeriodicalId\":425164,\"journal\":{\"name\":\"2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)\",\"volume\":\"91 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/SICE.2018.8492690\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/SICE.2018.8492690","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
TongueInput: Input Method by Tongue Gestures Using Optical Sensors Embedded in Mouthpiece
We proposed a system to recognize tongue gestures by mounting a mouthpiece embedded with an array of photo-reflective sensors, to measure the changes in distance between the tongue surface and the back of the upper teeth when the tongue moves. The system utilizes grayscale images of the sensor values to calculate the HOG feature descriptor and to use SVM to recognize the gesture. We conducted two experiments to evaluate the accuracy of the system to estimate 4 tongue positions and 4 tongue gestures, where we obtained a recognition rate of 85.67% for positions and 77.5% for gestures. However, we observed that we can improve the rate by improving the issues we discovered.