sEMG Signal-Based Lower Limb Movements Recognition Using Tunable Q-Factor Wavelet Transform and Kraskov Entropy

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2023-08-01 DOI:10.1016/j.irbm.2023.100773
C. Wei, H. Wang, B. Zhou, N. Feng, F. Hu, Y. Lu, D. Jiang, Z. Wang
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引用次数: 4

Abstract

Background

The recognition of lower limb movement has a wide range of applications in rehabilitation training, wearable exoskeleton control, and human activity monitoring. Surface electromyography (sEMG) signals can directly reflect the intention of human movement and can be used as the source of lower limb movement recognition. Literature reports have shown that extracting features from sEMG signals is the core of human movement recognition based on sEMG signals. However, how to effectively extract features from the sEMG signal of the lower limbs affected by body gravity is a difficult problem for the recognition of lower limb movement based on the sEMG signal.

Objectives

The main objective of this paper is to propose an efficient lower limb movement recognition model based on sEMG signals to accurately recognize the four lower limb movements.

Methods and results

We proposed a novel method of lower limbs movements recognition based on tunable Q-factor wavelet transform (TQWT) and Kraskov entropy (KrEn). Firstly, the sEMG signals of four different lower limb movements from twenty subjects were recorded by seven wearable sEMG signal sensors, and the recorded sEMG signals were denoised by multi-scale principal component analysis (MSPCA). Then, the denoised sEMG signal is decomposed into multiple sub-band signals by TQWT and the KrEn feature is extracted from each sub-band signal. Next, the representative features are selected from the extracted KrEn features by the minimum redundancy maximum relevance (mRMR) feature selection method. Finally, the four lower limb movements are recognized by three machine learning classifiers. Besides, to improve the recognition performance, a majority voting (MV) technology is proposed for the post-processing of decision flow. Experimental results show that the combination of TQWT, KrEn, and MV technology achieved the average recognition accuracy of 98.42% using the linear discriminant analysis (LDA) classifier.

Conclusion

The method proposed in this paper can recognize lower limb movements with high accuracy. Compared with existing methods, this method is more advanced and accurate, indicating that it has great application potential in rehabilitation training, wearable exoskeleton control, and daily activity monitoring.

Abstract Image

基于可调q因子小波变换和Kraskov熵的表面肌电信号下肢运动识别
背景下肢运动识别在康复训练、可穿戴外骨骼控制和人体活动监测等方面有着广泛的应用。表面肌电信号可以直接反映人体运动的意图,可以作为下肢运动识别的来源。文献报道表明,从表面肌电信号中提取特征是基于表面肌电信号的人体运动识别的核心。然而,如何有效地从受重力影响的下肢表面肌电信号中提取特征,是基于表面肌电信号识别下肢运动的难题。目的本文的主要目的是提出一种基于表面肌电信号的有效下肢运动识别模型,以准确识别四种下肢运动。方法和结果提出了一种基于可调Q因子小波变换(TQWT)和Kraskov熵(KrEn)的下肢运动识别新方法。首先,用7个可佩戴的表面肌电信号传感器记录20名受试者4种不同下肢运动的表面肌电信息,并用多尺度主成分分析(MSPCA)对记录的表面肌电进行去噪。然后,通过TQWT将去噪的sEMG信号分解为多个子带信号,并从每个子带信号中提取KrEn特征。接下来,通过最小冗余最大相关性(mRMR)特征选择方法从提取的KrEn特征中选择代表性特征。最后,通过三个机器学习分类器对四个下肢运动进行识别。此外,为了提高识别性能,提出了一种用于决策流后处理的多数投票(MV)技术。实验结果表明,使用线性判别分析(LDA)分类器,TQWT、KrEn和MV技术的组合实现了98.42%的平均识别准确率。结论本文提出的方法能够高精度地识别下肢运动。与现有方法相比,该方法更先进、更准确,表明其在康复训练、可穿戴外骨骼控制和日常活动监测方面具有巨大的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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