An EMG-driven musculoskeletal model for estimation of wrist kinematics using mirrored bilateral movement

Zhao, Yihui; Li, Zhenhong; Zhang, Zhiqiang; Qian, Kun; Xie, Shane

doi:10.1016/j.bspc.2022.104480

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…[29]. However, in order to optimize estimation performance, machine learning algorithms need a large amount of datasets [30]. [25], [26].…”

Section: Recent Development Of Emg Control Methodsmentioning

confidence: 99%

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Karis,

Kasdirin,

Abas

et al. 2023

Journal of Robotics and Control

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Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer.

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“…[29]. However, in order to optimize estimation performance, machine learning algorithms need a large amount of datasets [30]. [25], [26].…”

Section: Recent Development Of Emg Control Methodsmentioning

confidence: 99%

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Karis,

Kasdirin,

Abas

et al. 2023

Journal of Robotics and Control

View full text Add to dashboard Cite

show abstract

“…Studies [24]- [27] employed the rigid-tendon Hill model. Specifically, [24] and [25] balanced the Hill model's predictive accuracy and computational complexity through sensitivity analysis and GA optimization, addressing the oversimplified model that tends to overestimate parameters and thus neglect subject specificity. Moreover, based on the mirrored bilateral motion experiment, [25] proved that there is no statistical difference between the performance of this method on the ipsilateral side and the contralateral side.…”

Section: ) Wrist Jointsmentioning

confidence: 99%

“…Specifically, [24] and [25] balanced the Hill model's predictive accuracy and computational complexity through sensitivity analysis and GA optimization, addressing the oversimplified model that tends to overestimate parameters and thus neglect subject specificity. Moreover, based on the mirrored bilateral motion experiment, [25] proved that there is no statistical difference between the performance of this method on the ipsilateral side and the contralateral side. Additionally, since the supinator is the deep-seated muscle challenging to measure via sEMG directly, studies [26] and [27] employed Non-negative Matrix Factorization (NMF)-based virtual MS co-activation to replace the muscle activation of the pronator and supinator, subsequently inputting these into the Hill model for prediction.…”

Section: ) Wrist Jointsmentioning

confidence: 99%

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Wei,

Zhang,

Xie

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Upper limb functional impairments persisting after stroke significantly affect patients' quality of life. Precise adjustment of robotic assistance levels based on patients' motion intention using sEMG signals is crucial for active rehabilitation. This paper systematically reviews studies over the past decade on continuous prediction of upper limb single joint and multi-joint combinations motion intention using Model-Based (MB) and Model-Free (MF) approaches, 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 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. In summary, this review summarizes the current state and challenges of research, aiming to provide inspiration for future research on predicting upper limb motion intention based on sEMG.

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Forward dynamics simulation of a simplified neuromuscular-skeletal-exoskeletal model based on the CMA-ES optimization algorithm: framework and case studies

Jin,

Liu,

Zhang

et al. 2024

Multibody Syst Dyn

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An EMG-driven musculoskeletal model for estimation of wrist kinematics using mirrored bilateral movement

Cited by 5 publications

References 39 publications

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Forward dynamics simulation of a simplified neuromuscular-skeletal-exoskeletal model based on the CMA-ES optimization algorithm: framework and case studies

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