Complex Surface Electromyography Signal Gesture Recognition Based on Multipathway Featured Scale Convolutional Neural Network

IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-10-23 DOI:10.1109/TIM.2024.3485448
Tie Liu;Dianchun Bai;Le Ma;Qiang Du;Hiroshi Yokoi
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Abstract

Surface electromyography-based gesture recognition and prosthetic hand control using deep learning (DL) have become increasingly significant in the field of human-computer interaction. This study aims to enhance the control of prosthetic hands driven by complex gestures, addressing the challenge of low-resolution gesture differentiation caused by the coupling and superposition of surface electromyography signals in DL models. We propose a DL-based framework for the recognition of complex surface electromyography signals, utilizing a multipathway approach to acquire raw surface electromyography signals, process them in the time-frequency domain, and extract features using multiscale convolutional networks. The processed surface electromyography features are then analyzed in parallel to enhance accuracy. This method effectively processes multiple signals concurrently and extracts diverse feature sets. By collecting data from six channels, it achieves an 88.56% recognition rate for 16 complex hand gestures, enabling control of ten distinct prosthetic hand movements. By leveraging multidimensional continuous surface electromyography images, we have developed a feature model that resolves the issues of signal coupling and superposition in multichannel surface electromyography data, allowing for precise control of prosthetic hand movements.
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基于多通道特征尺度卷积神经网络的复杂表面肌电信号手势识别
基于表面肌电图的手势识别和使用深度学习(DL)的假手控制在人机交互领域变得越来越重要。本研究旨在增强由复杂手势驱动的假手控制,解决 DL 模型中表面肌电信号的耦合和叠加导致的低分辨率手势区分难题。我们提出了一种基于 DL 的复杂表面肌电信号识别框架,利用多途径方法获取原始表面肌电信号,在时频域对其进行处理,并使用多尺度卷积网络提取特征。然后对处理后的表面肌电图特征进行并行分析,以提高准确性。这种方法能有效地同时处理多个信号,并提取不同的特征集。通过收集来自六个通道的数据,它对 16 种复杂手势的识别率达到了 88.56%,能够控制十种不同的假手动作。通过利用多维连续表面肌电图图像,我们建立了一个特征模型,解决了多通道表面肌电图数据中的信号耦合和叠加问题,从而实现了对假手动作的精确控制。
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
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