首页|基于卷积神经网络的肌电信号人体运动模式识别技术

基于卷积神经网络的肌电信号人体运动模式识别技术

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随着外骨骼机器人等肌电控制设备的快速发展,表面肌电信号此类非平稳、非周期信号在高性能运动识别系统中的应用已成为相关研究领域的重点.为实现肌电信号跨域特征融合,提出一种基于肌电信号的双卷积链神经网络模型,采集7块关键肌肉的原始肌电信号,经特征提取,转化为能量核相图和离散小波变换系数特征图,分别输入双卷积链神经网络的卷积神经网络分支和MobileNetV2分支,利用融合模块提取高层隐藏特征并进行充分交互.制备包括以上两种特征图像和传统肌电信号图谱在内的3种数据集.3组交叉实验结果表明:所提方法对6种自测下肢运动的平均识别准确率达94.19％,显著优于其他特征组合与网络架构;在ENABL3S开源数据集识别7种下肢运动中取得98.32％的稳态识别准确率,进一步验证了所提方法优良的肌电特征捕捉能力和模式识别准确性.
Surface Electromyography-based Human Motion Pattern Recognition Using Convolutional Neural Networks
Along with the rapid development of surface electromyography(sEMG)-controlled devices,such as exoskeleton robots,the application of non-stationary and aperiodic signals in advanced high-performance motion recognition system has become a notable focus in relevant fields.To achieve the cross-domain feature fusion of sEMG,a dual convolutional chains neural network based on sEMG signals is proposed.Raw sEMG signals of seven key differentiated muscles are collected and processed by feature extraction methods,and converted into energy kernel phase diagram and discrete wavelet transform coefficient feature map,which are respectively input into the CNN branch and the MobileNetV2 branch of dual convolutional chains neural networks.The extracted high-level hidden features are processed by the fusion module for full interaction.Three datasets,including the above two feature images and conventional electromyography spectrum,are prepared.Three sets of cross experiments show that the average recognition accuracy of the proposed method for six self-tested lower limb movements is 94.19％,which is significantly better than other feature combinations and network architectures.Meanwhile,seven lower limb movements are identified with 98.32％steady-state recognition accuracy in ENABL3S open source dataset,further proving that the proposed method has excellent sEMG feature capture ability and pattern recognition accuracy.

exoskeleton robotssurface electromyographymotion pattern recognitiondual convolutional chains neural networkenergy kernelwavelet transform analysis

刘亚丽、鲁妍池、马勋举、宋遒志

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北京理工大学机电学院,北京 100081

北京理工大学前沿技术研究院外骨骼技术研发中心,山东济南 250300

外骨骼机器人 表面肌电信号 运动模式识别 双卷积链神经网络 能量核 小波变换分析

国家自然科学基金青年项目北京理工大学青年教师学术启动计划项目

51905035XSQD-202202001

2024

10.12382/bgxb.2023.0125

兵工学报
中国兵工学会

兵工学报

CSTPCD北大核心
影响因子：0.735
ISSN：1000-1093
年,卷(期)：2024.45(7)
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