{"title":"基于6DOF惯性传感器的头部手势识别","authors":"Ionut-Cristian Severin, D. Dobrea","doi":"10.1109/ISETC50328.2020.9301099","DOIUrl":null,"url":null,"abstract":"This paper proposed and investigated the head gesture recognition idea based on a single inertial sensor and deep learning approach. During the experiments presented in this paper, the main aim was to evaluate and determine the best deep learning models that can accurately recognize the head movements. The developed system can identify eight head gestures activities. The data collected in this work was done using a 6DOF inertial sensor placed on a headphone pair from 9 volunteers with ages between 20 and 40 years old. The results show that the best-proposed model's classification accuracy did reach a value equal to 93.64 % using raw data. In this experiment, each head gesture command's accuracy was in the range of 71.11 % to 100 %.","PeriodicalId":165650,"journal":{"name":"2020 International Symposium on Electronics and Telecommunications (ISETC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Head Gesture Recognition based on 6DOF Inertial sensor using Artificial Neural Network\",\"authors\":\"Ionut-Cristian Severin, D. Dobrea\",\"doi\":\"10.1109/ISETC50328.2020.9301099\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposed and investigated the head gesture recognition idea based on a single inertial sensor and deep learning approach. During the experiments presented in this paper, the main aim was to evaluate and determine the best deep learning models that can accurately recognize the head movements. The developed system can identify eight head gestures activities. The data collected in this work was done using a 6DOF inertial sensor placed on a headphone pair from 9 volunteers with ages between 20 and 40 years old. The results show that the best-proposed model's classification accuracy did reach a value equal to 93.64 % using raw data. In this experiment, each head gesture command's accuracy was in the range of 71.11 % to 100 %.\",\"PeriodicalId\":165650,\"journal\":{\"name\":\"2020 International Symposium on Electronics and Telecommunications (ISETC)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Symposium on Electronics and Telecommunications (ISETC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISETC50328.2020.9301099\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Symposium on Electronics and Telecommunications (ISETC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISETC50328.2020.9301099","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Head Gesture Recognition based on 6DOF Inertial sensor using Artificial Neural Network
This paper proposed and investigated the head gesture recognition idea based on a single inertial sensor and deep learning approach. During the experiments presented in this paper, the main aim was to evaluate and determine the best deep learning models that can accurately recognize the head movements. The developed system can identify eight head gestures activities. The data collected in this work was done using a 6DOF inertial sensor placed on a headphone pair from 9 volunteers with ages between 20 and 40 years old. The results show that the best-proposed model's classification accuracy did reach a value equal to 93.64 % using raw data. In this experiment, each head gesture command's accuracy was in the range of 71.11 % to 100 %.