Jiong Sun, S. Redyuk, E. Billing, D. Högberg, Paul E. Hemeren
{"title":"Tactile Interaction and Social Touch: Classifying Human Touch Using a Soft Tactile Sensor","authors":"Jiong Sun, S. Redyuk, E. Billing, D. Högberg, Paul E. Hemeren","doi":"10.1145/3125739.3132614","DOIUrl":null,"url":null,"abstract":"This paper presents an ongoing study on affective human-robot interaction. In our previous research, touch type is shown to be informative for communicated emotion. Here, a soft matrix array sensor is used to capture the tactile interaction between human and robot and 6 machine learning methods including CNN, RNN and C3D are implemented to classify different touch types, constituting a pre-stage to recognizing emotional tactile interaction. Results show an average recognition rate of 95% by C3D for classified touch types, which provide stable classification results for developing social touch technology.","PeriodicalId":346669,"journal":{"name":"Proceedings of the 5th International Conference on Human Agent Interaction","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 5th International Conference on Human Agent Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3125739.3132614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents an ongoing study on affective human-robot interaction. In our previous research, touch type is shown to be informative for communicated emotion. Here, a soft matrix array sensor is used to capture the tactile interaction between human and robot and 6 machine learning methods including CNN, RNN and C3D are implemented to classify different touch types, constituting a pre-stage to recognizing emotional tactile interaction. Results show an average recognition rate of 95% by C3D for classified touch types, which provide stable classification results for developing social touch technology.
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