{"title":"Movement intention decoding based on deep learning for multiuser myoelectric interfaces","authors":"Ki-Hee Park, Seong-Whan Lee","doi":"10.1109/IWW-BCI.2016.7457459","DOIUrl":null,"url":null,"abstract":"Recently, the development of practical myoelectric interfaces has resulted in the emergence of wearable rehabilitation robots such as arm prosthetics. In this paper, we propose a novel method of movement intention decoding based on the deep feature learning using electromyogram of human biosignals. In daily life, the inter-user variability cause decreases in performance by modulating target EMG patterns across different users. Therefore, we propose a user-adaptive decoding method for robust movement intention decoding in the inter-user variability, employing the convolutional neural network for the deep feature learning, trained by different users. In our experimental results, the proposed method predicted hand movement intention more accurately than a competing method.","PeriodicalId":208670,"journal":{"name":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"119","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 4th International Winter Conference on Brain-Computer Interface (BCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWW-BCI.2016.7457459","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 119
Abstract
Recently, the development of practical myoelectric interfaces has resulted in the emergence of wearable rehabilitation robots such as arm prosthetics. In this paper, we propose a novel method of movement intention decoding based on the deep feature learning using electromyogram of human biosignals. In daily life, the inter-user variability cause decreases in performance by modulating target EMG patterns across different users. Therefore, we propose a user-adaptive decoding method for robust movement intention decoding in the inter-user variability, employing the convolutional neural network for the deep feature learning, trained by different users. In our experimental results, the proposed method predicted hand movement intention more accurately than a competing method.