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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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Xu D, Zhou H, Quan W, Gusztav F, Baker JS, Gu Y. Adaptive neuro-fuzzy inference system model driven by the non-negative matrix factorization-extracted muscle synergy patterns to estimate lower limb joint movements. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107848. [PMID: 37863010 DOI: 10.1016/j.cmpb.2023.107848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/16/2023] [Accepted: 10/05/2023] [Indexed: 10/22/2023]
Abstract
BACKGROUND AND OBJECTIVE For patients with movement disorders, the main clinical focus is on exercise rehabilitation to help recover lost motor function, which is achieved by relevant assisted equipment. The basis for seamless control of the assisted equipment is to achieve accurate inference of the user's movement intentions in the human-machine interface. This study proposed a novel movement intention detection technology for estimating lower limb joint continuous kinematic variables following muscle synergy patterns, to develop applications for more efficient assisted rehabilitation training. METHODS This study recruited 16 healthy males and 16 male patients with symptomatic patellar tendinopathy (VISA-P: 59.1 ± 8.7). The surface electromyography of 12 muscles and lower limb joint kinematic and kinetic data from healthy subjects and patients during step-off landings from 30 cm-high stair steps were collected. We subsequently solved the preprocessed data based on the established recursive model of second-order differential equation to obtain the muscle activation matrix, and then imported it into the non-negative matrix factorization model to obtain the muscle synergy matrix. Finally, the lower limb neuromuscular synergy pattern was then imported into the developed adaptive neuro-fuzzy inference system non-linear regression model to estimate the human movement intention during this movement pattern. RESULTS Six muscle synergies were determined to construct the muscle synergy pattern driven ANFIS model. Three fuzzy rules were determined in most estimation cases. Combining the results of the four error indicators across the estimated variables indicates that the current model has excellent estimated performance in estimating lower limb joint movement. The estimation errors between the healthy (Angle: R2=0.98±0.03; Torque: R2=0.96±0.04) and patient (Angle: R2=0.98±0.02; Torque: R2=0.96±0.03) groups are consistent. CONCLUSION The proposed model of this study can accurately and reliably estimate lower limb joint movements, and the effectiveness will also be radiated to the patient group. This revealed that our models also have certain advantages in the recognition of motor intentions in patients with relevant movement disorders. Future work from this study can be focused on sports rehabilitation in the clinical field by achieving more flexible and precise movement control of the lower limb assisted equipment to help the rehabilitation of patients.
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Affiliation(s)
- Datao Xu
- Faculty of Sports Science, Ningbo University, Ningbo 315211, China; Faculty of Engineering, University of Pannonia, Veszprém 8201, Hungary; Savaria Institute of Technology, Eötvös Loránd University, Szombathely 9700, Hungary
| | - Huiyu Zhou
- Faculty of Sports Science, Ningbo University, Ningbo 315211, China; School of Health and Life Sciences, University of the West of Scotland, Scotland G72 0LH, United Kingdom
| | - Wenjing Quan
- Faculty of Sports Science, Ningbo University, Ningbo 315211, China; Faculty of Engineering, University of Pannonia, Veszprém 8201, Hungary; Savaria Institute of Technology, Eötvös Loránd University, Szombathely 9700, Hungary
| | - Fekete Gusztav
- Faculty of Engineering, University of Pannonia, Veszprém 8201, Hungary; Savaria Institute of Technology, Eötvös Loránd University, Szombathely 9700, Hungary
| | - Julien S Baker
- Department of Sport and Physical Education, Hong Kong Baptist University, Hong Kong 999077, China
| | - Yaodong Gu
- Faculty of Sports Science, Ningbo University, Ningbo 315211, China.
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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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Liang J, Shi Z, Zhu F, Chen W, Chen X, Li Y. Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals. Front Public Health 2021; 9:685596. [PMID: 34095080 PMCID: PMC8175857 DOI: 10.3389/fpubh.2021.685596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 04/20/2021] [Indexed: 11/22/2022] Open
Abstract
There is uncertainty in the neuromusculoskeletal system, and deterministic models cannot describe this significant presence of uncertainty, affecting the accuracy of model predictions. In this paper, a knee joint angle prediction model based on surface electromyography (sEMG) signals is proposed. To address the instability of EMG signals and the uncertainty of the neuromusculoskeletal system, a non-parametric probabilistic model is developed using a Gaussian process model combined with the physiological properties of muscle activation. Since the neuromusculoskeletal system is a dynamic system, the Gaussian process model is further combined with a non-linear autoregressive with eXogenous inputs (NARX) model to create a Gaussian process autoregression model. In this paper, the normalized root mean square error (NRMSE) and the correlation coefficient (CC) are compared between the joint angle prediction results of the Gaussian process autoregressive model prediction and the actual joint angle under three test scenarios: speed-dependent, multi-speed and speed-independent. The mean of NRMSE and the mean of CC for all test scenarios in the healthy subjects dataset and the hemiplegic patients dataset outperform the results of the Gaussian process model, with significant differences (p < 0.05 and p < 0.05, p < 0.05 and p < 0.05). From the perspective of uncertainty, a non-parametric probabilistic model for joint angle prediction is established by using Gaussian process autoregressive model to achieve accurate prediction of human movement.
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Affiliation(s)
- Jie Liang
- Department of Rehabilitation, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, China
| | - Zhengyi Shi
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, China
| | - Feifei Zhu
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, China
| | - Wenxin Chen
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, China
| | - Xin Chen
- Department of Rehabilitation, Fuzhou Second Hospital Affiliated to Xiamen University, Fuzhou, China
| | - Yurong Li
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China.,Fujian Key Laboratory of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, China
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Yu S, Chen Y, Cai Q, Ma K, Zheng H, Xie L. A Novel Quantitative Spasticity Evaluation Method Based on Surface Electromyogram Signals and Adaptive Neuro Fuzzy Inference System. Front Neurosci 2020; 14:462. [PMID: 32523505 PMCID: PMC7261936 DOI: 10.3389/fnins.2020.00462] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 04/15/2020] [Indexed: 02/02/2023] Open
Abstract
Stroke patients often suffer from spasticity. Before treatment of spasticity, there are often practical demands for objective and quantitative assessment of muscle spasticity. However, the common quantitative spasticity assessment method, the tonic stretch reflex threshold (TSRT), is time-consuming and complicated to implement due to the requirement of multiple passive stretches. To evaluate spasticity conveniently, a novel spasticity evaluation method based on surface electromyogram (sEMG) signals and adaptive neuro fuzzy inference system (i.e., the sEMG-ANFIS method) was presented in this paper. Eleven stroke patients with spasticity and four healthy subjects were recruited to participate in the experiment. During the experiment, the Modified Ashworth scale (MAS) scores of each subject was obtained and sEMG signals from four elbow flexors or extensors were collected from several times (4–5) repetitions of passive stretching. Four time-domain features (root mean square, the zero-cross rate, the wavelength and a 4th-order autoregressive model coefficient) and one frequency-domain feature (the mean power frequency) were extracted from the collected sEMG signals to reflect the spasticity information. Using the ANFIS classifier, excellent regression performance was achieved [mean accuracy = 0.96, mean root-mean-square error (RMSE) = 0.13], outperforming the classical TSRT method (accuracy = 0.88, RMSE = 0.28). The results showed that the sEMG-ANFIS method not only has higher accuracy but also is convenient to implement by requiring fewer repetitions (4–5) of passive stretches. The sEMG-ANFIS method can help stroke patients develop proper rehabilitation training programs and can potentially be used to provide therapeutic feedback for some new spasticity interventions, such as shockwave therapy and repetitive transcranial magnetic stimulation.
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Affiliation(s)
- Song Yu
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Yan Chen
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
| | - Qing Cai
- Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ke Ma
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China
| | - Haiqing Zheng
- Department of Rehabilitation Medicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Longhan Xie
- Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China
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