[Enhancement algorithm for surface electromyographic-based gesture recognition based on real-time fusion of muscle fatigue features].

Q4 Medicine
Shijia Yan, Ye Yang, Peng Yi
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引用次数: 0

Abstract

This study aims to optimize surface electromyography-based gesture recognition technique, focusing on the impact of muscle fatigue on the recognition performance. An innovative real-time analysis algorithm is proposed in the paper, which can extract muscle fatigue features in real time and fuse them into the hand gesture recognition process. Based on self-collected data, this paper applies algorithms such as convolutional neural networks and long short-term memory networks to provide an in-depth analysis of the feature extraction method of muscle fatigue, and compares the impact of muscle fatigue features on the performance of surface electromyography-based gesture recognition tasks. The results show that by fusing the muscle fatigue features in real time, the algorithm proposed in this paper improves the accuracy of hand gesture recognition at different fatigue levels, and the average recognition accuracy for different subjects is also improved. In summary, the algorithm in this paper not only improves the adaptability and robustness of the hand gesture recognition system, but its research process can also provide new insights into the development of gesture recognition technology in the field of biomedical engineering.

[基于肌肉疲劳特征实时融合的表面肌电图手势识别增强算法]。
本研究旨在优化基于表面肌电图的手势识别技术,重点关注肌肉疲劳对识别性能的影响。本文提出了一种创新的实时分析算法,可实时提取肌肉疲劳特征并将其融合到手势识别过程中。基于自收集的数据,本文应用卷积神经网络和长短期记忆网络等算法深入分析了肌肉疲劳的特征提取方法,并比较了肌肉疲劳特征对基于表面肌电图的手势识别任务性能的影响。结果表明,通过实时融合肌肉疲劳特征,本文提出的算法提高了不同疲劳程度下的手势识别准确率,不同受试者的平均识别准确率也有所提高。总之,本文的算法不仅提高了手势识别系统的适应性和鲁棒性,而且其研究过程也能为生物医学工程领域手势识别技术的发展提供新的启示。
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来源期刊
生物医学工程学杂志
生物医学工程学杂志 Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
4868
期刊介绍:
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