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semg-Based Lower Limb Motion Prediction Using CNN-LSTM with Improved PCA Optimization Algorithm

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Journal of Bionic Engineering 2023年第2期20卷 612-627页

作者： Meng Zhu Xiaorong Guan Zhong Li Long He Zheng Wang Keshu Cai School of Mechanical Engineering Nanjing University of Science and TechnologyNo.200XiaolingweiNanjing CityJiangsu ProvinceChina Department of Rehabilitation Medicine The First Affiliated Hospital of Nanjing Medical University300 Guangzhou RoadNanjing CityJiangsu ProvinceChina

In recent years,semg(surface electromyography)signals have been increasingly used to operate wearable *** development of mechanical lower limbs or exoskeletons controlled by the nervous system requires greater accuracy in recognizing lower limb *** is less research on devices to assist the human body in uphill ***,developing controllers that can accurately predict and control human upward movements in real-time requires the employment of appropriate signal pre-processing methods and prediction *** this purpose,this paper investigates the effects of various semg pre-processing methods and algorithms on the prediction *** investigation involved ten subjects(five males and five females)with normal knee *** relevant data of the subjects were collected on a constructed *** obtain feature values that reflect the gait characteristics,an improved PCA algorithm based on the kernel method is proposed for dimensionality reduction to remove redundant ***,a new model(CNN+LSTM)was proposed to predict the knee joint *** approaches were utilized to validate the superiority of the improved PCA method and CNN-LSTM *** feasibility of the method was verified by analyzing the gait prediction results of different ***,the prediction time of the method was 25 ms,and the prediction error was 1.34±0.25 *** comparing with traditional machine learning methods such as BP(backpropagation)neural network,RF(random forest),and SVR(support vector machine),the improved PCA algorithm processed data performed the best in terms of convergence time and prediction accuracy in CNN-LSTM *** experimental results demonstrate that the proposed method(improved PCA+CNN-LSTM)effectively recognizes lower limb activity from semg *** the same data input,the EMG signal processed using the improved PCA method performed better in terms of prediction *** is the first step toward myoelectri

关键词： CNN LSTM Exoskeleton semg Knee trajectory PCA Bionic robot

TFN-FICFM:semg-Based Gesture Recognition Using Temporal Fusion Network and Fuzzy Integral-based Classifier Fusion

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Journal of Bionic Engineering 2024年第4期21卷 1878-1891页

作者： Fo Hu Kailun He Mengyuan Qian Mohamed Amin Gouda College of Information Engineering Zhejiang University of TechnologyHangzhou310023China Department of Mechanical Engineering and Automation Northeastern UniversityShenyang110819China

Surface electromyography(semg)-based gesture recognition is a key technology in the field of human–computer ***,existing gesture recognition methods face challenges in effectively integrating discriminative temporal feature representations from semg *** this paper,we propose a deep learning framework named TFN-FICFM comprises a Temporal Fusion Network(TFN)and Fuzzy Integral-Based Classifier Fusion method(FICFM)to improve the accuracy and robustness of gesture ***,we design a TFN module,which utilizes an attention-based recurrent multi-scale convolutional module to acquire multi-level temporal feature representations and achieves deep fusion of temporal features through a feature pyramid ***,the deep-fused temporal features are utilized to generate multiple sets of gesture category prediction confidences through a feedback ***,we employ FICFM to perform fuzzy fusion on prediction confidences,resulting in the ultimate *** study conducts extensive comparisons and ablation studies using the publicly available datasets Ninapro DB2 and *** demonstrate that the TFN-FICFM model outperforms state-of-the-art methods in classification *** research can serve as a benchmark for semg-based gesture recognition and related deep learning modeling.

关键词： Gesture recognition semg Deep learning Temporal fusion Fuzzy fusion

Forehead semg signal based HMI for hands-free control

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The Journal of China Universities of Posts and Telecommunications 2014年第3期21卷 98-105页

作者： ZHANG Yi ZHU Xiang DAI Ling-ling LUO Yuan National Engineering Research and Development Center for Information Accessibility Chongqing University of Posts and Telecommunications

A hands-free method is proposed to control an electric powered wheelchair （EPW） based on surface electromyography （semg） signals. A CyberLink device is deployed to obtain and analyze forehead semg signals generated by the facial movements. The autoregressive （AR） model is used to extract semg features. Then, the back-propagation artificial neural network （BPANN） is proposed to recognize different facial movement patterns and improved by Bayesian regularization and Levenberg-Marquardt （LM） algorithm. A semg based human-machine interface （HMI） is designed to map facial movement patterns into corresponding control commands. The experimental results show that the method is simple, real-time and have a high recognition rate.

关键词： intelligent wheelchair semg HMI AR model BP artificial neural network

A Novel SE-CNN Attention Architecture for semg-Based Hand Gesture Recognition

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Computer Modeling in Engineering & Sciences 2023年第1期134卷 157-177页

作者： Zhengyuan Xu Junxiao Yu Wentao Xiang Songsheng Zhu Mubashir Hussain Bin Liu Jianqing Li Key Laboratory of Clinical and Medical Engineering School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing211166China Department of Medical Engineering Wannan Medical CollegeWuhu241002China Postdoctoral Innovation Practice Shenzhen PolytechnicShenzhen518055China

In this article,to reduce the complexity and improve the generalization ability of current gesture recognition systems,we propose a novel SE-CNN attention architecture for semg-based hand gesture *** proposed algorithm introduces a temporal squeeze-and-excite block into a simple CNN architecture and then utilizes it to recalibrate the weights of the feature outputs from the convolutional *** enhancing important features while suppressing useless ones,the model realizes gesture recognition *** last procedure of the proposed algorithm is utilizing a simple attention mechanism to enhance the learned representations of semg signals to performmulti-channel semg-based gesture recognition *** evaluate the effectiveness and accuracy of the proposed algorithm,we conduct experiments involving multi-gesture datasets Ninapro DB4 and Ninapro DB5 for both inter-session validation and subject-wise *** a series of comparisons with the previous models,the proposed algorithm effectively increases the robustness with improved gesture recognition performance and generalization ability.

关键词： Hand gesture recognition semg CNN temporal squeeze-and-excite attention

An epidermal semg tattoo-like patch as a new human–machine interface for patients with loss of voice

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Microsystems & Nanoengineering 2020年第1期6卷 1209-1221页

作者： Huicong Liu Wei Dong Yunfei Li Fanqi Li Jiangjun Geng Minglu Zhu Tao Chen Hongmiao Zhang Lining Sun Chengkuo Lee School of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced RoboticsSoochow University215123 SuzhouChina Department of Electrical&Computer Engineering National University of Singapore4 Engineering Drive 3Singapore 117576Singapore National University of Singapore Suzhou Research Institute(NUSRI) Suzhou Industrial Park215123 SuzhouChina

Throat cancer treatment involves surgical removal of the tumor,leaving patients with facial disfigurement as well as temporary or permanent loss of *** electromyography(semg)generated from the jaw contains lots of voice ***,it is difficult to record because of not only the weakness of the signals but also the steep skin *** paper demonstrates the design of an imperceptible,flexible epidermal semg tattoo-like patch with the thickness of less than 10μm and peeling strength of larger than 1N cm−1 that exhibits large adhesiveness to complex biological surfaces and is thus capable of semg recording for silent speech *** a tester speaks silently,the patch shows excellent performance in recording the semg signals from three muscle channels and recognizing those frequently used instructions with high accuracy by using the wavelet decomposition and pattern *** average accuracy of action instructions can reach up to 89.04%,and the average accuracy of emotion instructions is as high as 92.33%.To demonstrate the functionality of tattoo-like patches as a new human–machine interface(HMI)for patients with loss of voice,the intelligent silent speech recognition,voice synthesis,and virtual interaction have been implemented,which are of great importance in helping these patients communicate with people and make life more enjoyable.

关键词： semg patients patch

semg-Based Hand Motion Recognition System Using RMSR and AR Model 36

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sEMG-Based Hand Motion Recognition System Using RMSR and AR ...

基于下肢semg的疲劳模糊增量熵表征方法研究

第36届中国控制会议

作者： Xina Ren Yew Guan Soo Masato Odagaki Feng Duan College of Computer and Control Engineering Nankai University Universiti Teknikal Malaysia Melaka Maebashi Institute of Technology

ISBN: (纸本)9781538629185

Surface electromyogram（semg） signals have been used to control multifunctional prosthetic *** usually focused on the use of several channels with semg signals to identify more hand motions without limiting the number of semg ***,the residual muscles of an amputee are ***,the point of a successful recognition system is to decrease the channels of semg signals to classify more hand *** achieve this goal,we proposed a hand motion recognition system,in which three channels of semg signals can classify nine hand *** this recognition system,the time domain features,root mean square ratio（RMSR） and autoregressive（AR） model,were selected to extract the features of the semg signals as compared with the time-frequency domain ***,the linear discriminant analysis（LDA） was adopted as the ***,the average accuracy rate of the presented system was 91.46%.Therefore,the proposed algorithms in this paper can be reasonably feasible for prosthetic hands.

关键词： semg RMSR AR model LDA hand motion recognition system

来源： cnki会议评论

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仪器仪表学报 2024年第5期45卷 271-280页

作者：石欣余可祺敖钰民秦鹏杰张杰毅重庆大学自动化学院重庆400044 中国科学院深圳先进技术研究院深圳518055 四川电力职业技术学院成都611133

连续运动中,基于表面肌电信号(semg)外骨骼机器人与人进行协同运动控制,肌肉产生疲劳将影响人机协同控制的柔顺性及鲁棒性。本文创新性地提出模糊增量熵(EIFEn)用以表征肌肉疲劳程度,并对肌肉疲劳阶段的较为客观划分;采集人体连续抬腿... 详细信息

连续运动中,基于表面肌电信号(semg)外骨骼机器人与人进行协同运动控制,肌肉产生疲劳将影响人机协同控制的柔顺性及鲁棒性。本文创新性地提出模糊增量熵(EIFEn)用以表征肌肉疲劳程度,并对肌肉疲劳阶段的较为客观划分;采集人体连续抬腿运动中下肢12块肌肉的表面肌电信号,提出基于变异性敏感系数SVR肌肉疲劳敏感度判断方式,实现有效肌肉选取,提出基于均模积的自适应阈值动作切分法,将完整信号切分并提取单个动作信号序列,通过分析计算,对疲劳趋势进行表征。实验结果表明,本文模型相比时域频域算法具有较为明显的肌肉疲劳表征梯度特征,与fApEn及FFDispEn相比具有较好的疲劳表征能力,用于疲劳等级聚类的戴维森堡丁指数(DBI)为0.39,可提高外骨骼人机协同控制,为实现疲劳分阶段补偿助力提供参考。

关键词： semg 肌肉疲劳动作切分模糊增量熵特征提取聚类

基于semg和肌肉骨骼模型的手指多关节力矩耦合分析

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仪器仪表学报 2022年第9期43卷 266-275页

作者：谢平肖俊明于金须张立杰杜义浩燕山大学电气工程学院河北省测试计量技术及仪器重点实验室秦皇岛066004 燕山大学机械工程学院河北省重型机械流体动力传输与控制实验室秦皇岛066004

手指内部力矩受表面肌电信号、肌力、手部姿态等因素影响而无法直接获取,为了实时且准确地获取手指各关节力矩以及耦合力矩并应用于手部康复机器人的交互控制中,提出了一种基于表面肌电信号和肌肉骨骼模型的手指多关节力矩和耦合力矩分... 详细信息

手指内部力矩受表面肌电信号、肌力、手部姿态等因素影响而无法直接获取,为了实时且准确地获取手指各关节力矩以及耦合力矩并应用于手部康复机器人的交互控制中,提出了一种基于表面肌电信号和肌肉骨骼模型的手指多关节力矩和耦合力矩分析与实时获取方法。首先设计了自适应手指关节角度采集系统,通过实验同步采集指浅屈肌与指伸肌的肌电信号以及手指各关节的角度数据,建立手指多关节力矩模型,从而获取手指各关节力矩。然后建立手指D-H模型,结合虚功原理获取手指的耦合力矩。最后,辨识了手指多关节力矩模型的参数,并通过OpenSim软件获取了仿真力矩。计算力矩与仿真力矩的对比结果显示:4名被试3个关节力矩的均方根误差分别为0.1567、0.097425、0.08495,证明了该方法能够实时并准确的获取手指各关节力矩和耦合力矩,能够满足手部康复机器人交互控制准确性和实时性的需求。

关键词：单关节力矩 semg 关节角度肌肉骨骼模型耦合力矩 D-H模型交互控制

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基于遗传算法的semg至SFAP的分解算法

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中国生物医学工程学报 2005年第3期24卷 343-349页

作者：王江李韬费向阳曾启明张立天津大学电气与自动化工程学院天津300072 香港理工大学电机工程系

本研究提出了一种表面肌电信号(SurfaceElectromyogram ,semg)至单纤维动作电位(SingleFiberActionPotential,SFAP)新的分解算法。由于semg分解的复杂性,本研究将semg分解问题转化为SFAP三基函数参数的优化问题和同一SFAP参数的聚类问... 详细信息

本研究提出了一种表面肌电信号(SurfaceElectromyogram ,semg)至单纤维动作电位(SingleFiberActionPotential,SFAP)新的分解算法。由于semg分解的复杂性,本研究将semg分解问题转化为SFAP三基函数参数的优化问题和同一SFAP参数的聚类问题。在算法中,运用改进的遗传算法(GeneticAlgorithm ,GA)进行参数的优化,运用无监督学习的Kohonen神经网络进行参数的聚类。遗传算法的运用加强了算法的搜索能力,提高了分解的正确率,加快了算法的收敛速度。本分解算法的运用使得医疗诊断和假肢控制等领域可以通过非侵入式测量得到SFAP随时间的变化规律。

关键词： semg 遗传算法分解 SFAP 聚类