EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network.

IF 2.6 4区 工程技术 Q1 Mathematics
Hafiz Ghulam Murtza Qamar, Muhammad Farrukh Qureshi, Zohaib Mushtaq, Zubariah Zubariah, Muhammad Zia Ur Rehman, Nagwan Abdel Samee, Noha F Mahmoud, Yeong Hyeon Gu, Mohammed A Al-Masni
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引用次数: 0

Abstract

This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to assess the effectiveness of the proposed DP-CNN architecture across three datasets (NinaPro DB1, DB2, and DB3), encompassing both able-bodied and amputee subjects. Performance metrics, including accuracy, precision, recall, and F1-score, are employed for comprehensive evaluation. The DP-CNN demonstrates notable mean accuracies of 94.93 ± 1.71% and 94.00 ± 3.65% on NinaPro DB1 and DB2 for healthy subjects, respectively. Additionally, it achieves a robust mean classification accuracy of 85.36 ± 0.82% on amputee subjects in DB3, affirming its efficacy. Comparative analysis with previous methodologies on the same datasets reveals substantial improvements of 28.33%, 26.92%, and 39.09% over the baseline for DB1, DB2, and DB3, respectively. The DP-CNN's superior performance extends to comparisons with transfer learning models for image classification, reaffirming its efficacy. Across diverse datasets involving both able-bodied and amputee subjects, the DP-CNN exhibits enhanced capabilities, holding promise for advancing myoelectric control.

利用双通路卷积神经网络对 EMG 手势信号进行分析,以诊断上肢疾病。
本研究介绍了一种新颖的双通路卷积神经网络(DP-CNN)架构,该架构专为在从原始多通道肌电信号导出的 Log-Mel 频谱图像分析中实现稳健性能而量身定制。主要目的是评估拟议的 DP-CNN 架构在三个数据集(NinaPro DB1、DB2 和 DB3)中的有效性,其中包括健全受试者和截肢受试者。综合评估采用了准确度、精确度、召回率和 F1 分数等性能指标。DP-CNN 对健康受试者的 NinaPro DB1 和 DB2 的平均准确率分别为 94.93 ± 1.71% 和 94.00 ± 3.65%。此外,在 DB3 中,它对截肢受试者的平均分类准确率达到了 85.36 ± 0.82%,证明了它的功效。在相同的数据集上与以前的方法进行比较分析后发现,DB1、DB2 和 DB3 比基线分别提高了 28.33%、26.92% 和 39.09%。DP-CNN 的卓越性能还延伸到与图像分类的迁移学习模型的比较中,再次证明了它的功效。在涉及健全受试者和截肢受试者的各种数据集上,DP-CNN 显示出更强的能力,为推进肌电控制带来了希望。
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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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