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2024 | OriginalPaper | Buchkapitel

A DNN-Based Learning Framework for Continuous Movements Segmentation

verfasst von : Tian-yu Xiang, Xiao-Hu Zhou, Xiao-Liang Xie, Shi-Qi Liu, Zhen-Qiu Feng, Mei-Jiang Gui, Hao Li, Zeng-Guang Hou

Erschienen in: Neural Information Processing

Verlag: Springer Nature Singapore

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Abstract

This study presents a novel experimental paradigm for collecting Electromyography (EMG) data from continuous movement sequences and a Deep Neural Network (DNN) learning framework for segmenting movements from these signals. Unlike prior research focusing on individual movements, this approach characterizes human motion as continuous sequences. The DNN framework comprises a segmentation module for time point level labeling of EMG data and a transfer module predicting movement transition time points. These outputs are integrated based on defined rules. Experimental results reveal an impressive capacity to accurately segment movements, evidenced by segmentation metrics (accuracy: \(88.3\%\); Dice coefficient: \(82.9\%\); mIoU: \(72.7\%\)). This innovative approach to time point level analysis of continuous movement sequences via EMG signals offers promising implications for future studies of human motor functions and the advancement of human-machine interaction systems.

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Titel: A DNN-Based Learning Framework for Continuous Movements Segmentation
verfasst von: Tian-yu Xiang
Xiao-Hu Zhou
Xiao-Liang Xie
Shi-Qi Liu
Zhen-Qiu Feng
Mei-Jiang Gui
Hao Li
Zeng-Guang Hou
Verlag: Springer Nature Singapore
Buch: Neural Information Processing
Print ISBN: 978-981-9980-66-6

Electronic ISBN: 978-981-9980-67-3

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-8067-3_30

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