近日,中国科学院沈阳自动化研究所在非理想手势肌电信号数据集构建方面研究取得新进展。相关成果以SeNic: An Open Source Dataset for SEMG-Based Gesture Recognition in Non-Ideal Conditions为题发表在康复医学领域一区Top期刊IEEE Transactions on Neural Systems and Rehabilitation Engineering上。
沈阳自动化所医疗康复机器人研究组长期专注于脑/肌电信号解码、人机/脑机智能交互等关键技术及系统研发,在非理想脑肌电信号识别、运动意图解码、肌电交互、脑机接口等方面的研究取得多项创新成果。近年来多篇研究成果发表在国际知名学术期刊IEEE Transactions on Neural Systems and Rehabilitation Engineering (2021,2022),Biomedical Signal Processing and Control (2022),Frontiers in Neurorobotics (2021),IEEE Journal of Biomedical and Health Informatics (2021),Journal of Neural Engineering (2020),Frontiers in Neuroscience (2018),以及IEEE Transactions on Systems, Man, and Cybernetics: Systems (2016),相关成果获得国内外研究人员的广泛关注。
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