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Vijayvargiya A, Sinha A, Gehlot N, Jena A, Kumar R, Moran K. S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality. PLoS One 2024; 19:e0301263. [PMID: 38820390 PMCID: PMC11142505 DOI: 10.1371/journal.pone.0301263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/13/2024] [Indexed: 06/02/2024] Open
Abstract
The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.
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Affiliation(s)
- Ankit Vijayvargiya
- Insight Science Foundation Ireland Research Centre for Data Analytics, School of Human and Health Performance, Dublin City University, Dublin, Ireland
- Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, Rajasthan, India
| | - Aparna Sinha
- Department of Information Technology, Bansthali Vidyapeeth, Radha Kishnpura, Rajasthan, India
| | - Naveen Gehlot
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Ashutosh Jena
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Rajesh Kumar
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Kieran Moran
- Insight Science Foundation Ireland Research Centre for Data Analytics, School of Human and Health Performance, Dublin City University, Dublin, Ireland
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Wei Z, Zhang ZQ, Xie SQ. Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1487-1504. [PMID: 38557618 DOI: 10.1109/tnsre.2024.3383857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Upper limb functional impairments persisting after stroke significantly affect patients' quality of life. Precise adjustment of robotic assistance levels based on patients' motion intentions using sEMG signals is crucial for active rehabilitation. This paper systematically reviews studies on continuous prediction of upper limb single joints and multi-joint combinations motion intention using Model-Based (MB) and Model-Free (MF) approaches over the past decade, based on 186 relevant studies screened from six major electronic databases. The findings indicate ongoing challenges in terms of subject composition, algorithm robustness and generalization, and algorithm feasibility for practical applications. Moreover, it suggests integrating the strengths of both MB and MF approaches to improve existing algorithms. Therefore, future research should further explore personalized MB-MF combination methods incorporating deep learning, attention mechanisms, muscle synergy features, motor unit features, and closed-loop feedback to achieve precise, real-time, and long-duration prediction of multi-joint complex movements, while further refining the transfer learning strategy for rapid algorithm deployment across days and subjects. Overall, this review summarizes the current research status, significant findings, and challenges, aiming to inspire future research on predicting upper limb motion intentions based on sEMG.
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Li S, Zhang L, Meng Q, Yu H. A Real-Time Control Method for Upper Limb Exoskeleton Based on Active Torque Prediction Model. Bioengineering (Basel) 2023; 10:1441. [PMID: 38136032 PMCID: PMC10741095 DOI: 10.3390/bioengineering10121441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 11/29/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023] Open
Abstract
Exoskeleton rehabilitation robots have been widely used in the rehabilitation treatment of stroke patients. Clinical studies confirmed that rehabilitation training with active movement intentions could improve the effectiveness of rehabilitation treatment significantly. This research proposes a real-time control method for an upper limb exoskeleton based on the active torque prediction model. To fulfill the goal of individualized and precise rehabilitation, this method has an adjustable parameter assist ratio that can change the strength of the assist torque under the same conditions. In this study, upper limb muscles' EMG signals and elbow angle were chosen as the sources of control signals. The active torque prediction model was then trained using a BP neural network after appropriately extracting features. The model exhibited good accuracy on PC and embedded systems, according to the experimental results. In the embedded system, the RMSE of this model was 0.1956 N·m and 94.98%. In addition, the proposed real-time control system also had an extremely low delay of only 40 ms, which would significantly increase the adaptability of human-computer interactions.
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Affiliation(s)
- Sujiao Li
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China; (S.L.); (L.Z.); (Q.M.)
- Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, China
- Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai 200093, China
| | - Lei Zhang
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China; (S.L.); (L.Z.); (Q.M.)
- Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, China
| | - Qiaoling Meng
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China; (S.L.); (L.Z.); (Q.M.)
- Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, China
- Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai 200093, China
| | - Hongliu Yu
- Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China; (S.L.); (L.Z.); (Q.M.)
- Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, China
- Key Laboratory of Neural-Functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai 200093, China
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Liu K, Ji S, Liu Y, Gao C, Zhang S, Fu J, Dai L. Analysis of Ankle Muscle Dynamics during the STS Process Based on Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:6607. [PMID: 37514901 PMCID: PMC10385903 DOI: 10.3390/s23146607] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/18/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023]
Abstract
Ankle joint moment is an important indicator for evaluating the stability of the human body during the sit-to-stand (STS) movement, so a method to analyze ankle joint moment is needed. In this study, a wearable sensor system that could derive surface-electromyography (sEMG) signals and kinematic signals on the lower limbs was developed for non-invasive estimation of ankle muscle dynamics during the STS movement. Based on the established ankle joint musculoskeletal information and sEMG signals, ankle joint moment during the STS movement was calculated. In addition, based on a four-segment STS dynamic model and kinematic signals, ankle joint moment during the STS movement was calculated using the inverse dynamics method. Ten healthy young people participated in the experiment, who wore a self-developed wearable sensor system and performed STS movements as an experimental task. The results showed that there was a high correlation (all R ≥ 0.88) between the results of the two methods for estimating ankle joint moment. The research in this paper can provide theoretical support for the development of an intelligent bionic joint actuator and clinical rehabilitation evaluation.
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Affiliation(s)
- Kun Liu
- School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
| | - Shuo Ji
- School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
| | - Yong Liu
- School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
| | - Chi Gao
- School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
| | - Shizhong Zhang
- School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
| | - Jun Fu
- School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
| | - Lei Dai
- School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
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Xu R, Zhao X, Wang Z, Zhang H, Meng L, Ming D. A Co-driven Functional Electrical Stimulation Control Strategy by Dynamic Surface Electromyography and Joint Angle. Front Neurosci 2022; 16:909602. [PMID: 35898409 PMCID: PMC9309284 DOI: 10.3389/fnins.2022.909602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/13/2022] [Indexed: 11/29/2022] Open
Abstract
Functional electrical stimulation (FES) is widely used in neurorehabilitation to improve patients’ motion ability. It has been verified to promote neural remodeling and relearning, during which FES has to produce an accurate movement to obtain a good efficacy. Therefore, many studies have focused on the relationship between FES parameters and the generated movements. However, most of the relationships have been established in static contractions, which leads to an unsatisfactory result when applied to dynamic conditions. Therefore, this study proposed a FES control strategy based on the surface electromyography (sEMG) and kinematic information during dynamic contractions. The pulse width (PW) of FES was determined by a direct transfer function (DTF) with sEMG features and joint angles as the input. The DTF was established by combing the polynomial transfer functions of sEMG and joint torque and the polynomial transfer functions of joint torque and FES. Moreover, the PW of two FES channels was set based on the muscle synergy ratio obtained through sEMG. A total of six healthy right-handed subjects were recruited in this experiment to verify the validity of the strategy. The PW of FES applied to the left arm was evaluated based on the sEMG of the right extensor carpi radialis (ECR) and the right wrist angle. The coefficient of determination (R2) and the normalized root mean square error (NRMSE) of FES-included and voluntary wrist angles and torques were used to verify the performance of the strategy. The result showed that this study achieved a high accuracy (R2 = 0.965 and NRMSE = 0.047) of joint angle and a good accuracy (R2 = 0.701 and NRMSE = 0.241) of joint torque reproduction during dynamic movements. Moreover, the DTF in real-time FES system also had a nice performance of joint angle fitting (R2 = 0.940 and NRMSE = 0.071) and joint torque fitting (R2 = 0.607 and NRMSE = 0.303). It is concluded that the proposed strategy is able to generate proper FES parameters based on sEMG and kinematic information for dynamic movement reproduction and can be used in a real-time FES system combined with bilateral movements for better rehabilitation.
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Affiliation(s)
- Rui Xu
- Laboratory of Motor Rehabilitation, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Xinyu Zhao
- Laboratory of Motor Rehabilitation, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Ziyao Wang
- Laboratory of Motor Rehabilitation, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Hengyu Zhang
- Laboratory of Motor Rehabilitation, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Lin Meng
- Laboratory of Motor Rehabilitation, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- *Correspondence: Lin Meng,
| | - Dong Ming
- Laboratory of Motor Rehabilitation, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Dong Ming,
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AKHIL VM, ASHMI M, JOBIN V, RAJENDRAKUMAR PK, SIVANANDAN KS. ESTIMATION OF KNEE JOINT TORQUE DURING SIT–STAND MOVEMENT BASED ON sEMG SIGNALS USING NEURAL NETWORKS. J MECH MED BIOL 2022. [DOI: 10.1142/s0219519422500245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The estimation of knee joint torque is important for the development of powered exoskeletons to achieve ideal gait characteristics. In this study, we proposed three different models to predict the required torque for performing sit-to-stand (STS) and back-to-sit (BTS) movements. The surface electromyography (sEMG) signals were extracted from the biceps femoris and rectus femoris muscles during STS and BTS movements. The time-domain features selected as input to the models for torque prediction are integrated EMG (iEMG), root mean square (RMS), and mean absolute value (MAV). Two-way ANOVA analysis identifies the significance of NN models and EMG features of the muscles in predicting the knee joint torque requirement. The artificial neural network models selected for prediction are the feed-forward back-propagation algorithm, ANFIS, and NARX. The theoretical value of knee joint torque calculated using the Lagrange method was compared with the torque output for each model based on root mean square error (RMSE). The desired torque predicted using the NARX model confirms to have the least average error ([Formula: see text][Formula: see text]Nm), which indicates that NARX can estimate knee joint torque more accurately from sEMG than other models.
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Affiliation(s)
- V. M. AKHIL
- Mechanical Engineering Department, National Institute of Engineering, Mysore 570008, India
| | - M. ASHMI
- Electrical Engineering Department, National Institute of Technology, Calicut 673601, India
| | - V. JOBIN
- Mechanical Engineering Department, Adi Shankara Institute of Engineering and Technology, Kerala 683574, India
| | - P. K. RAJENDRAKUMAR
- Mechanical Engineering Department, National Institute of Technology, Calicut 673601, India
| | - K. S. SIVANANDAN
- Biomedical Engineering Department, Manipal Institute of Technology, Karnataka 576104, India
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Zhang L, Long J, Zhao R, Cao H, Zhang K. Estimation of the Continuous Pronation–Supination Movement by Using Multichannel EMG Signal Features and Kalman Filter: Application to Control an Exoskeleton. Front Bioeng Biotechnol 2022; 9:771255. [PMID: 35299701 PMCID: PMC8921927 DOI: 10.3389/fbioe.2021.771255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 12/27/2021] [Indexed: 11/23/2022] Open
Abstract
The Hill muscle model can be used to estimate the human joint angles during continuous movement. However, adopting this model requires the knowledge of many parameters, such as the length and speed of contraction of muscle fibers, which are liable to change with different individuals, leading to errors in estimation. This study established the backpropagation neural network model based on surface electromyography (sEMG) features and human movement angle. First, the function of muscles in joint rotation is defined, and then, sensors are placed on muscle tissues to gain sEMG, and then, a relation model between the surface sEMG features and the joint angle is constructed. As integrated electromyography information cannot be well reflected through a single electromyography feature, a feature extraction method combining the time domain, frequency domain, and time–frequency domain was proposed. As the degree of freedom (DOF) of the pronation–supination movement was controlled by several muscles, it was difficult to make an angle prediction. A method of correcting the estimation error based on the Kalman filter was raised to cope with this problem. An exoskeleton robot with one DOF was designed and put into the tracking experiment. The results show that the proposed model was able to enhance the estimation of the joint angle during continuous pronation–supination movements.
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Ling ZQ, Cao GZ, Zhang YP, Cheng HR, He BB, Cao SB. Real-time Knee Joint Angle Estimation Based on Surface Electromyograph and Back Propagation Neural Network. 2021 18TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS (UR) 2021. [DOI: 10.1109/ur52253.2021.9494639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning. MACHINES 2021. [DOI: 10.3390/machines9030056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification.
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