EMG-Based Real-Time Linear-Nonlinear Cascade Regression Decoding of Shoulder, Elbow, and Wrist Movements in Able-Bodied Persons and Stroke Survivors

Liu, Jie; Ren, Yupeng; Xu, Danfeng; Kang, Sang Hoon; Zhang, Li‐Qun

doi:10.1109/tbme.2019.2935182

Cited by 57 publications

(30 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Power et al [154] determined dynamic time waping (DTW) of the RMS value of the signal yielded higher accuracy and lower computational cost than the TDPSD feature set . Liu et al [155] used a linear-nonlinear cascade regression to simultaneously estimate shoulder, elbow, and wrist joint angles accounting for 93%, 90%, and 84% of the variance in able-bodied subjects, and 85%, 91% and 85% of the variance in stroke subjects, respectively. Betthauser et al [135] validated the use of a sparse representation classification (SRC), which had found prior success in image detection in cases of heavily occluded objects or missing pixels.…”

Section: Robust Algorithmsmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

View full text Add to dashboard Cite

This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

show abstract

Section: Robust Algorithmsmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Power et al [158] determined dynamic time waping (DTW) of the RMS value of the signal yielded higher accuracy and lower computational cost than the TDPSD feature set . Liu et al [159] used a linear-nonlinear cascade regression to simultaneously estimate shoulder, elbow, and wrist joint angles accounting for 93%, 90%, and 84% of the variance in able-bodied subjects, and 85%, 91% and 85% of the variance in stroke subjects, respectively. Betthauser et al [140] validated the use of a sparse representation classification (SRC), which had found prior success in image detection in cases of heavily occluded objects or missing pixels.…”

Section: Robust Algorithmsmentioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Preprint

View full text Add to dashboard Cite

Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the performance of such devices exceeds 90\% in controlled environments, myoelectric devices still face challenges in robustness to variability of daily living conditions. Within this survey, the intrisic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where performance was 13% and 20% lower in realistic environments compared to controlled environments for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

show abstract

“…The surface electromyography (sEMG) reflects the electrical activity of muscle fibres during contraction, and it has been widely used for intelligent prostheses or exoskeleton robotics control [1,2]. To decode human intentions from sEMG more intuitively, artificial intelligence (AI) can be leveraged in either the classification-based hand gesture recognition [3,4] or regression-based kinematic estimation [5,6]. Different from the classification scheme which is only able to estimate discrete movements sequentially [7], regression approaches estimate continuous joint motions and can enable simultaneous and proportional control in multiple degrees of freedoms [8].…”

Section: Introductionmentioning

confidence: 99%

Inter-Subject Domain Adaptation for CNN-Based Wrist Kinematics Estimation Using sEMG

Bao

Zaidi

Xie

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Recently, convolutional neural network (CNN) has been widely investigated to decode human intentions using surface Electromyography (sEMG) signals. However, a pre-trained CNN model usually suffers from severe degradation when testing on a new individual, and this is mainly due to domain shift where characteristics of training and testing sEMG data differ substantially. To enhance inter-subject performances of CNN in the wrist kinematics estimation, we propose a novel regression scheme for supervised domain adaptation (SDA), based on which domain shift effects can be effectively reduced. Specifically, a two-stream CNN with shared weights is established to exploit source and target sEMG data simultaneously, such that domaininvariant features can be extracted. To tune CNN weights, both regression losses and a domain discrepancy loss are employed, where the former enable supervised learning and the latter minimizes distribution divergences between two domains. In this study, eight healthy subjects were recruited to perform wrist flexion-extension movements. Experiment results illustrated that the proposed regression SDA outperformed fine-tuning, a stateof-the-art transfer learning method, in both single-single and multiple-single scenarios of kinematics estimation. Unlike finetuning which suffers from catastrophic forgetting, regression SDA can maintain much better performances in original domains, which boosts the model reusability among multiple subjects.

show abstract

EMG-Based Real-Time Linear-Nonlinear Cascade Regression Decoding of Shoulder, Elbow, and Wrist Movements in Able-Bodied Persons and Stroke Survivors

Cited by 57 publications

References 56 publications

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Inter-Subject Domain Adaptation for CNN-Based Wrist Kinematics Estimation Using sEMG

Contact Info

Product

Resources

About