Subject-Independent Continuous Estimation of sEMG-Based Joint Angles Using Both Multisource Domain Adaptation and BP Neural Network

Li, He; Guo, Shuxiang; Wang, Hanze; Bu, Dongdong

doi:10.1109/tim.2022.3225015

Cited by 8 publications

(7 citation statements)

References 27 publications

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“…The learning rate is set as 0.0001 with the 1000 epochs to train the AE network. To test the discriminant network, the Pearson correlation (PC) [ 16 ] is applied to measure the difference between the input data and the reconstructed data as shown in Equation (11).

where

and

represent the

dimension values of the

input vector

and the reconstructed vector

, respectively, and

and

denote average of

and

, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…sEMG signals can directly reflect the activation of superficial muscles with rich limp motion control information. In recent years, sEMG signal as the exoskeleton control signal source has been widely applied to a rehabilitation estimation [ 8 , 9 , 10 ], human intention prediction [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ], and rehabilitation robot control [ 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

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Low-Density sEMG-Based Pattern Recognition of Unrelated Movements Rejection for Wrist Joint Rehabilitation

Guo

et al. 2023

Micromachines

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sEMG-based pattern recognition commonly assumes a limited number of target categories, and the classifiers often predict each target category depending on probability. In wrist rehabilitation training, the patients may make movements that do not belong to the target category unconsciously. However, most pattern recognition methods can only identify limited patterns and are prone to be disturbed by abnormal movement, especially for wrist joint movements. To address the above the problem, a sEMG-based rejection method for unrelated movements is proposed to identify wrist joint unrelated movements using center loss. In this paper, the sEMG signal collected by the Myo armband is used as the input of the sEMG control method. First, the sEMG signal is processed by sliding signal window and image coding. Then, the CNN with center loss and softmax loss is used to describe the spatial information from the sEMG image to extract discriminative features and target movement recognition. Finally, the deep spatial information is used to train the AE to reject unrelated movements based on the reconstruction loss. The results show that the proposed method can realize the target movements recognition and reject unrelated movements with an F-score of 93.4% and a rejection accuracy of 95% when the recall is 0.9, which reveals the effectiveness of the proposed method.

show abstract

where

and

represent the

dimension values of the

input vector

and the reconstructed vector

, respectively, and

and

denote average of

and

, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-Density sEMG-Based Pattern Recognition of Unrelated Movements Rejection for Wrist Joint Rehabilitation

Guo

et al. 2023

Micromachines

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show abstract

“…Moreover, [63] highlighted the superior performance of RBFNN in predicting both joint angles and angular velocities compared to MLP. Numerous studies, including [67]- [71], employed BPNN for prediction. Specifically, [67] showed that inter-subject variability significantly impacts the predictive performance by comparing generic and personalized models.…”

Section: B Mf Approaches 1) Shoulder Joints A) Traditional Neural Net...mentioning

confidence: 99%

“…Specifically, [67] showed that inter-subject variability significantly impacts the predictive performance by comparing generic and personalized models. Similarly, [71] evidenced greater inter-subject variability than intra-subject by comparing TD features and subject-invariant features extracted using Maximum Independent Domain Adaptation (MIDA). Consequently, [68] incorporated the GA feature selection to eliminate the inter-subject redundant and low-correlation features, thereby enhancing the inter-subject generalizability.…”

Section: B Mf Approaches 1) Shoulder Joints A) Traditional Neural Net...mentioning

confidence: 99%

“…Considering the inherent physiological and muscle control strategy differences among subjects, inter-subject variability significantly exceeds intra-subject variability [67], [71]. Moreover, based on the fact that extracting highly correlated inter-subject features from multi-subject training can improve model generalizability, and current MB and MF methods' lengthy training times preclude the rapid deployment across subjects [68], the capability for effective transfer learning becomes crucial.…”

Section: ) Transfer Learningmentioning

confidence: 99%

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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.

show abstract

An EMG-Based Biomimetic Variable Stiffness Modulation Strategy for Bilateral Motor Skills Relearning of Upper Limb Elbow Joint Rehabilitation

et al. 2023

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Subject-Independent Continuous Estimation of sEMG-Based Joint Angles Using Both Multisource Domain Adaptation and BP Neural Network

Cited by 8 publications

References 27 publications

Low-Density sEMG-Based Pattern Recognition of Unrelated Movements Rejection for Wrist Joint Rehabilitation

Low-Density sEMG-Based Pattern Recognition of Unrelated Movements Rejection for Wrist Joint Rehabilitation

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

An EMG-Based Biomimetic Variable Stiffness Modulation Strategy for Bilateral Motor Skills Relearning of Upper Limb Elbow Joint Rehabilitation

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