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Deep Learning based sEMG decoding for TelePresence device control

文献类型： 会议论文

第一作者： ádám Gyula Nemes

作者： ádám Gyula Nemes 1 ; Levente Kovács 2 ; Gy?rgy Eigner 2 ; Gergely Márton 3 ; Béla Weiss 4 ;

作者机构： 1.Physiological Controls Research Center, óbuda University|Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, óbuda University|Applied Informatics and Applied Mathematics Doctoral School, óbuda University, Bécsi út 96/B, Budapest, Hungary

2.Physiological Controls Research Center, óbuda University|Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, óbuda University

3.Integrative Neuroscience Research Group, Institute of Cognitive Neuroscience and Physiology, Research Centre for Natural Sciences

4.Physiological Controls Research Center, óbuda University|Brain Imaging Centre, Research Centre for Natural Sciences, Magyar tudósok k?rútja 2., Budapest, Hungary

关键词： Headphones;Visualization;Telepresence;Recurrent neural networks;Robot vision systems;Process control;Virtual reality

会议名称： International Conference on Process Control

主办单位：

页码： 192-197

摘要： TelePresence devices enable their users to appear and move at distant locations. In this work, to achieve TelePresence, we installed a 360° camera on a mobile robot and streamed the video feed to a virtual reality headset. The user’s view field is outsourced, which is disadvantageous for traditional control methods. To overcome this issue, here we designed an sEMG-based control paradigm, enabling users to control a robot with hand gestures. We created a dataset of the necessary control commands, by recording the visual representation of the hand and the corresponding sEMG activity. The final dataset consisted of 5200 train and 400 validation samples. We investigated several neural network architectures to decode the biosignals and finally applied a CNN + LSTM Recurrent Neural Network to control a mobile robot by hand sEMG signals.

分类号： tp27-53

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