A force levels and gestures integrated multi-task strategy for neural decoding

Hua, Shaoyang; Wang, Congqing; Xie, Zuoshu; Wu, Xuewei

doi:10.1007/s40747-020-00140-9

Cited by 9 publications

(8 citation statements)

References 27 publications

(35 reference statements)

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“…The reason for the degraded EMG-PR performance is that when the muscle force level is changed, the corresponding amplitude and frequency characteristics of the EMG signal also change depending on the physiology of the muscle [22]. The EMG signal amplitude changes more dominantly than the frequency-domain characteristics [11], [27].…”

Section: Discussionmentioning

confidence: 99%

“…However, prosthetic hand users expect the highest EMG-PR performance in nearly all scenarios, and thus, the performance level should be more than 90% [26]. To achieve the minimum satisfactory performance, Hua et al [27] introduced multitask learning (MTL) to simultaneously recognize gestures and force levels. The authors indicated that the combination of frequency-domain features and convolutional neural networks (CNNs) was more appropriate than amplitude-based features.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Signal Normalization Approach to Improve the Force Invariant Myoelectric Pattern Recognition of Transradial Amputees

et al. 2021

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Variation in the electromyogram pattern recognition (EMG-PR) performance with the muscle contraction force is a key limitation of the available prosthetic hand. To alleviate this problem, we propose a scheme to realize electromyogram signal normalization across channels before feature extraction. The proposed signal normalization scheme is validated over a dataset of nine transradial amputees that includes three force levels with six hand gestures. Moreover, we employ three classifiers, namely, linear discriminant analysis (LDA), support vector machine (SVM) and k-nearest neighbour (KNN), to evaluate the EMG-PR performance. In addition to the signal normalization scheme, we perform nonlinear transformation of the features by using the logarithm function. Both schemes facilitate merging of the muscle activation patterns of different force levels. The experimental results indicate that the force invariant EMG-PR performance (F1 score of at least 3.24% to 4.34%) of the proposed schemes is significantly enhanced compared to that obtained in recent studies. Therefore, we recommend using these features along with the proposed signal normalization scheme and nonlinear transformation of the features to improve the force invariant EMG-PR performance. The proposed feature extraction method achieves the highest F1 score of 91.28%, 91.39% and 90.56% when using the LDA, SVM and KNN classifiers, respectively.INDEX TERMS EMG Pattern recognition, Force invariant features, Muscle activation pattern, Signal normalization.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Signal Normalization Approach to Improve the Force Invariant Myoelectric Pattern Recognition of Transradial Amputees

et al. 2021

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“…The window width was 128 ms and the sliding distance was 78 ms. We selected 13 specific features including zero-crossings (ZC), root mean square (RMS), mean absolute value (MAV), waveform length (WL), variance (VAR), slope sign change (SSC), Willison amplitude (WAMP), mean value (MEAN), the standard value (STD), mean frequency (MNF), median frequency (MF), mean power frequency (MPF), and Lempel-Ziv complexity (LZC). The ZC, RMS, MAV, WL, VAR, SSC, and WAMP have commonly used time-domain features (Hua et al, 2020 ; Qu et al, 2020 ; Wu et al, 2020 ; Duan et al, 2021 ). In addition to these parameters, we further calculated the MEAN and STD, so that the time-domain features reached nine.…”

Section: Methodsmentioning

confidence: 99%

Control of Newly-Designed Wearable Robotic Hand Exoskeleton Based on Surface Electromyographic Signals

Zeng³

et al. 2021

Front. Neurorobot.

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The human hand plays a role in a variety of daily activities. This intricate instrument is vulnerable to trauma or neuromuscular disorders. Wearable robotic exoskeletons are an advanced technology with the potential to remarkably promote the recovery of hand function. However, the still face persistent challenges in mechanical and functional integration, with real-time control of the multiactuators in accordance with the motion intentions of the user being a particular sticking point. In this study, we demonstrated a newly-designed wearable robotic hand exoskeleton with multijoints, more degrees of freedom (DOFs), and a larger range of motion (ROM). The exoskeleton hand comprises six linear actuators (two for the thumb and the other four for the fingers) and can realize both independent movements of each digit and coordinative movement involving multiple fingers for grasp and pinch. The kinematic parameters of the hand exoskeleton were analyzed by a motion capture system. The exoskeleton showed higher ROM of the proximal interphalangeal and distal interphalangeal joints compared with the other exoskeletons. Five classifiers including support vector machine (SVM), K-near neighbor (KNN), decision tree (DT), multilayer perceptron (MLP), and multichannel convolutional neural networks (multichannel CNN) were compared for the offline classification. The SVM and KNN had a higher accuracy than the others, reaching up to 99%. For the online classification, three out of the five subjects showed an accuracy of about 80%, and one subject showed an accuracy over 90%. These results suggest that the new wearable exoskeleton could facilitate hand rehabilitation for a larger ROM and higher dexterity and could be controlled according to the motion intention of the subjects.

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“…Deep-learning algorithms, specifically convolutional neural network (CNN) [27,28] and recurrent neural network (RNN) [29,30], have recently shown preferable results in searching the complex input-output relationship than traditional methods. Xu et al [31] analyzed the feasibility by applying CNN and RNN to sEMG-based force estimation.…”

Section: Introductionmentioning

confidence: 99%

A novel sEMG-based force estimation method using deep-learning algorithm

Hua

Wang

2021

Complex Intell. Syst.

Self Cite

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This paper discusses the problem of force estimation represented by surface electromyography (sEMG) signals collected from an armband-like collection device. The scheme is proposed for the sake of two dimensions of sEMG signals: spatial and temporal information. From the point of space, first, appropriate channel number across all subjects is investigated. During this progress, an electrode channel selection method based on Spearman’s rank order correlation coefficient is utilized to detect signals from active muscle. Then, to reduce the computation and highlight the channel information, linear regression (LR) algorithm is conducted to weight each channel. Besides, the recurrent neural network (RNN) is used to capture the temporal information and model the relation between sEMG and output force. Experiments conducted on four subjects demonstrate that six channels are enough to characterize the muscle activity. By combining the selected channels with different weight coefficients, LR algorithm can fit the output force better than simply averaging them. Furthermore, RNN with long short-term memory cell shows the superiority in time series modeling, which can improve our results to a greater degree. Experimental results prove the feasibility of the proposed method.

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A force levels and gestures integrated multi-task strategy for neural decoding

Cited by 9 publications

References 27 publications

A Novel Signal Normalization Approach to Improve the Force Invariant Myoelectric Pattern Recognition of Transradial Amputees

A Novel Signal Normalization Approach to Improve the Force Invariant Myoelectric Pattern Recognition of Transradial Amputees

Control of Newly-Designed Wearable Robotic Hand Exoskeleton Based on Surface Electromyographic Signals

A novel sEMG-based force estimation method using deep-learning algorithm

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