Tomasz Kocejko, Krzysztof Czuszyński, J. Rumiński, A. Bujnowski, A. Poliński, J. Wtorek
{"title":"通过实现肌电模块,扩展与智能眼镜的非触摸交互","authors":"Tomasz Kocejko, Krzysztof Czuszyński, J. Rumiński, A. Bujnowski, A. Poliński, J. Wtorek","doi":"10.1109/HSI.2017.8004988","DOIUrl":null,"url":null,"abstract":"In this paper we propose to use temporal muscle contraction to perform certain actions. Method: The set of muscle contractions corresponding to one of three actions including “single-click”, “double-click” “click-n-hold” and “non-action” were recorded. After recording certain amount of signals, the set of five parameters was calculated. These parameters served as an input matrix for the neural network. Two-layer feedforward neural network with one hidden layer of 200 neurons was applied to classify gestures based on the input matrix. Results: The network was trained using the dataset consisted of 43 samples and then tested on the 34 samples dataset. All gestures from the test set were correctly classified.","PeriodicalId":355011,"journal":{"name":"2017 10th International Conference on Human System Interactions (HSI)","volume":"248 6","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Extending touch-less interaction with smart glasses by implementing EMG module\",\"authors\":\"Tomasz Kocejko, Krzysztof Czuszyński, J. Rumiński, A. Bujnowski, A. Poliński, J. Wtorek\",\"doi\":\"10.1109/HSI.2017.8004988\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose to use temporal muscle contraction to perform certain actions. Method: The set of muscle contractions corresponding to one of three actions including “single-click”, “double-click” “click-n-hold” and “non-action” were recorded. After recording certain amount of signals, the set of five parameters was calculated. These parameters served as an input matrix for the neural network. Two-layer feedforward neural network with one hidden layer of 200 neurons was applied to classify gestures based on the input matrix. Results: The network was trained using the dataset consisted of 43 samples and then tested on the 34 samples dataset. All gestures from the test set were correctly classified.\",\"PeriodicalId\":355011,\"journal\":{\"name\":\"2017 10th International Conference on Human System Interactions (HSI)\",\"volume\":\"248 6\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 10th International Conference on Human System Interactions (HSI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HSI.2017.8004988\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 10th International Conference on Human System Interactions (HSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HSI.2017.8004988","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extending touch-less interaction with smart glasses by implementing EMG module
In this paper we propose to use temporal muscle contraction to perform certain actions. Method: The set of muscle contractions corresponding to one of three actions including “single-click”, “double-click” “click-n-hold” and “non-action” were recorded. After recording certain amount of signals, the set of five parameters was calculated. These parameters served as an input matrix for the neural network. Two-layer feedforward neural network with one hidden layer of 200 neurons was applied to classify gestures based on the input matrix. Results: The network was trained using the dataset consisted of 43 samples and then tested on the 34 samples dataset. All gestures from the test set were correctly classified.