Continuous Estimation of Finger and Wrist Joint Angles Using a Muscle Synergy Based Musculoskeletal Model

He, Zixun; Qin, Zixuan; Koike, Yasuharu

doi:10.3390/app12083772

Cited by 13 publications

(8 citation statements)

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“…Using this research, future work will install the sEMG measurement and automatically control the motor with the calculated tension from sEMG signals. The missing muscle signals are proposed to be predicted from the remaining muscles using the muscle synergy methods [24][25][26][27]. In our previous works, we developed a method for estimating myoelectric signals and arm trajectories and forces in real time from electrocorticogram (ECoG) [28][29][30][31][32] and control of robotic arms using joint angles and myoelectric signals predicted by ECoG [33].…”

Section: Discussionmentioning

confidence: 99%

A Design of Biomimetic Prosthetic Hand

et al. 2022

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Many patients with upper limb defects desire myoelectric prosthetic hands, but they are still not used for some reasons. One of the most significant reasons is its external appearance, which has the discomfort caused by the structural difference between a human hand and a robotic link. The structure must be based on human anatomy to create a more natural-looking prosthesis. This study designed a biomimetic prosthetic hand with bones, ligaments, tendons, and multiple muscles based on the human musculoskeletal system. We verified the proposed prosthetic hand using the viscoelastic angle sensor to determine whether it works like a human hand. We also compared the finger force of the prosthetic hand with that of a human finger. It could be capable of controlling the angle and the stiffness of the joint by multiple extensor and flexor muscles, like humans.

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Section: Discussionmentioning

confidence: 99%

A Design of Biomimetic Prosthetic Hand

et al. 2022

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“…For Task 1, participants needed to wear Perception Neuron glove, the measured angles, raw sEMG signals, estimated joint angle and the system outputs were acquired via LSL for evaluation. Since this was a regression prediction task, we chose Pearson correlation coefficient (CC) (Taylor, 1990 ; Stapornchaisit et al, 2019 ; Qin et al, 2021 ; He et al, 2022 ) as the metric to evaluate the prediction accuracy:…”

Section: Methodsmentioning

confidence: 99%

A CW-CNN regression model-based real-time system for virtual hand control

Qin

et al. 2022

Front. Neurorobot.

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For upper limb amputees, wearing a myoelectric prosthetic hand is the only way for them to continue normal life. Even until now, the proposal of a high-precision and natural performance real-time control system based on surface electromyography (sEMG) signals is still challenging. Researchers have proposed many strategies for motion classification or regression prediction tasks based on sEMG signals. However, most of them have been limited to offline analysis only. There are even few papers on real-time control based on deep learning models, almost all of which are about motion classification. Rare studies tried to use deep learning-based regression models in real-time control systems for multi-joint angle estimation via sEMG signals. This paper proposed a CW-CNN regression model-based real-time control system for virtual hand control. We designed an Adaptive Kalman Filter to smooth the joint angles output before sending them as control commands to control a virtual hand. Eight healthy participants were invited, and three sessions experiments were conducted on two different days for all of them. During the real-time experiment, we analyzed the joint angles estimation accuracy and computational latency. Moreover, target achievement control (TAC) test was applied to emphasize motion regression in real-time. The experimental results show that the proposed control system has high precision for 3-DOFs motion regression in simultaneously, and the system remains stable and low computational latency. In the future, the proposed real-time control system can be applied to actual prosthetic hand.

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“…Regarding the MSK model built on MS features and MU neural features, study [41] suggested that the Synergistic Linear Regression Model (SLRM) based on Hierarchical Alternating Least Squares (HALS) and LR slightly surpassed traditional MSK approaches. Studies [42], [43] integrated MS features with MSK models, where [42] modeled MSs extracted through NMF-HP with L2 regularization constraint (NMF-HP-L2) into the GO-optimized Hill model, achieving superior predictive performance and stability over both Hill and NMF-Hill models. [43] constructed the MSK model using MSs extracted from independent components obtained via Adaptive Mixture Independent Component Analysis (AMICA) with NMF, resulting in better predictive performance than traditional MSK models, LR, and SVR.…”

Section: ) Hand-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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Continuous Estimation of Finger and Wrist Joint Angles Using a Muscle Synergy Based Musculoskeletal Model

Cited by 13 publications

References 50 publications

A Design of Biomimetic Prosthetic Hand

A Design of Biomimetic Prosthetic Hand

A CW-CNN regression model-based real-time system for virtual hand control

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

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