基于表面肌电信号的迁移学习增强跨主体手势识别

IF 1.6 4区 医学 Q4 ENGINEERING, BIOMEDICAL
Shenyilang Zhang, Yinfeng Fang, Jiacheng Wan, Guozhang Jiang, Gongfa Li
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引用次数: 0

摘要

目的探讨人机交互(HMI)中人体物理动作分类的新兴领域,在帮助残疾人和机器人方面具有潜在的应用前景。研究重点是解决不同表面肌电信号带来的挑战,旨在提高跨主体手势识别。方法利用深度迁移学习技术,利用数值矩阵灰度转换和RGB映射生成的多特征图像(MFI)。这些小额信贷作为输入输入到一个微调的AlexNet模型中。两个数据库,ISRMyo-I和Ninapro DB1,被用于实验。进行了严格的测试,以确定最佳参数和特征组合。应用数据增强技术,使MFI数据集增加一倍。交叉受试者实验包括6种来自Ninapro DB1的手腕手势和13种来自ISRMyo-I的手势。结果该研究显示了显著的性能增强。在Ninapro DB1中,平均准确率达到86.16%,比性能最好的传统解码方法提高了13.25%。同样,在ISRMyo-I中,平均准确率达到70.41%,与传统方法相比准确率提高了7.4%。本研究建立了一个鲁棒的框架,能够减轻基于表面肌电信号的手势识别中的跨用户差异。采用深度迁移学习技术和多特征图像处理,显著提高了跨主体手势识别的准确率。这一进步有望丰富人机交互,并扩展该技术在帮助残疾人和机器人方面的实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Transfer Learning Enhanced Cross-Subject Hand Gesture Recognition with sEMG

Transfer Learning Enhanced Cross-Subject Hand Gesture Recognition with sEMG

Purpose

This study explores the emerging field of human physical action classification within human–machine interaction (HMI), with potential applications in assisting individuals with disabilities and robotics. The research focuses on addressing the challenges posed by diverse sEMG signals, aiming for improved cross-subject hand gesture recognition.

Methods

The proposed approach utilizes deep transfer learning technology, employing multi-feature images (MFI) generated through grayscale conversion and RGB mapping of numerical matrices. These MFIs are fed as input into a fine-tuned AlexNet model. Two databases, ISRMyo-I and Ninapro DB1, are employed for experimentation. Rigorous testing is conducted to identify optimal parameters and feature combinations. Data augmentation techniques are applied, doubling the MFI dataset. Cross-subject experiments encompass six wrist gestures from Ninapro DB1 and thirteen gestures from ISRMyo-I.

Results

The study demonstrates substantial performance enhancements. In Ninapro DB1, the mean accuracy achieves 86.16%, showcasing a 13.25% improvement over the best-performing traditional decoding method. Similarly, in ISRMyo-I, a mean accuracy of 70.41% is attained, signifying a 7.4% increase in accuracy compared to traditional methods.

Conclusion

This research establishes a robust framework capable of mitigating cross-user differences in hand gesture recognition based on sEMG signals. By employing deep transfer learning techniques and multi-feature image processing, the study significantly enhances the accuracy of cross-subject hand gesture recognition. This advancement holds promise for enriching human–machine interaction and extending the practical applications of this technology in assisting disabled individuals and robotics.

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来源期刊
CiteScore
4.30
自引率
5.00%
发文量
81
审稿时长
3 months
期刊介绍: The purpose of Journal of Medical and Biological Engineering, JMBE, is committed to encouraging and providing the standard of biomedical engineering. The journal is devoted to publishing papers related to clinical engineering, biomedical signals, medical imaging, bio-informatics, tissue engineering, and so on. Other than the above articles, any contributions regarding hot issues and technological developments that help reach the purpose are also included.
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