Estimation of continuous and constraint-free 3 DoF wrist movements from surface electromyogram signal using kernel recursive least square tracker

Bakshi, Koushik; Manjunatha, M.; Kumar, C. S.

doi:10.1016/j.bspc.2018.06.012

Cited by 18 publications

(15 citation statements)

References 25 publications

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“…3(b), where estimated angles are capable of matching the real data in 3 DOFs effectively. The 2 R values of CNN in #1DOF, #2DOF and #3DOF are 0.75, 0.70 and 0.66 respectively, which are consistent with related results achieved by other techniques in [3][4]13]. However estimation errors are still noticeable in complex muscle contractions, particularly for pronation and supination.…”

Section: A Wrist Angles Estimationsupporting

confidence: 88%

“…In past two decades, numerous semi-unsupervised and supervised, linear and nonlinear classical regression methods have been investigated in both offline and online scenarios [2][3][4], and a variety of feature selection methods [5] have also been exploited to obtain more discriminative model inputs. Although plenty of promising results have been achieved in laboratory settings, few practical implementations have shown up yet, mainly due to the inadequate prediction accuracy particularly during complex muscle contractions [3]. Classical ML techniques rely deeply on the manual feature extraction, analysis and selection, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Surface-EMG based Wrist Kinematics Estimation using Convolutional Neural Network

Bao

Zaidi

Xie

et al. 2019

2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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In the past decades, classical machine learning (ML) methods have been widely investigated in wrist kinematics estimation for the control of prosthetic hands. Currently deeper structures have shown great potential to further improve prediction accuracy. In this paper we present a single stream convolutional neural network (CNN) for mapping surface electromyography (sEMG) to wrist angles within three degrees-of-freedom (DOFs). Two types of two dimensional (2D) sEMG images are constructed in time domain and spectrum as CNN inputs, respectively. Six typical linear and nonlinear ML models are implemented for comparison, where four efficient time-spatial hand-crafted features are extracted to represent feature engineering. Experiment results with four able-bodied participants illustrate that CNN with 2D spectrum sEMG images can achieve highest accuracy in most testing sessions. In other sessions, it is still competitive to the most promising ML techniques. The core strength of deep learning (DL), i.e. feature learning via deep structures and efficient algorithms, is verified to be more powerful than classical feature engineering, particularly in smaller datasets.

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Section: A Wrist Angles Estimationsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Surface-EMG based Wrist Kinematics Estimation using Convolutional Neural Network

Bao

Zaidi

Xie

et al. 2019

2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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show abstract

“…On the contrary, P4 aimed at multi-DoF tasks and all 3 DoFs were involved simultaneously. Obviously, P4 is naturally more complex and challenging compared with P1-P3 [38], but it bears closer similarity with real-life movements [8] and can Sinusoidal contractions Pronation-supination (P-S) P3…”

Section: A Experiments Setupmentioning

confidence: 99%

“…Unlike PR-based methods which discriminate hand gestures in a discrete and sequential manner [3], regression models focus on continuous wrist kinematics estimation [4] and thus can promote simultaneous and proportional control in multiple degrees of freedoms (DoF). Several MLbased regression methods, including linear regression (LR), artificial neural network (ANN), kernel ridge regression, support vector regression (SVR) and random forest (RF), have been extensively exploited in both off-line simulations [5][6][7][8][9] and real-time prosthetic control [1]. However, ML techniques rely deeply on manual feature extraction [10], i.e.…”

Section: Introductionmentioning

confidence: 99%

A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography

Bao

Zaidi

Xie

et al. 2021

IEEE Trans. Instrum. Meas.

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Convolutional neural network (CNN) has been widely exploited for simultaneous and proportional myoelectric control due to its capability of deriving informative, representative and transferable features from surface electromyography (sEMG). However, muscle contractions have strong temporal dependencies but conventional CNN can only exploit spatial correlations. Considering that long short-term memory neural network (LSTM) is able to capture long-term and non-linear dynamics of time-series data, in this paper we propose a CNN-LSTM hybrid model to fully explore the temporal-spatial information in sEMG. Firstly, CNN is utilized to extract deep features from sEMG spectrum, then these features are processed via LSTM-based sequence regression to estimate wrist kinematics. Six healthy participants are recruited for the participatory collection and motion analysis under various experimental setups. Estimation results in both intra-session and inter-session evaluations illustrate that CNN-LSTM significantly outperforms CNN, LSTM and several representative machine learning approaches, particularly when complex wrist movements are activated. Index Terms-sEMG, wrist kinematics estimation, deep learning, convolutional neural network, long short-term memory network, hybrid model.

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“…To reduce the dimensionality of signal features, Phinyomark et al [23] have proposed thirty-seven time-domain and frequency-domain features for studying the characteristics of EMG signals, among which the most recommended ones are the mean absolute value, waveform length, Wilson amplitude, auto-regressive, mean absolute value slope, and mean frequency. Bakshi et al [24] have employed a Kernel Least Square Tracker (KRLS-T) to estimate dimensional wrist kinematics from the sEMG signals of forearm muscle groups. But the accuracy is not high enough.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification

Wang

et al. 2019

Applied Sciences

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It is necessary to complete the two parts of gesture recognition and wireless remote control to realize the gesture control of the automatic pruning machine. To realize gesture recognition, in this paper, we have carried out the research of gesture recognition technology based on surface electromyography signal, and discussed the influence of different numbers and different gesture combinations on the optimal size. We have calculated the 630-dimensional eigenvector from the benchmark scientific database of sEMG signals and extracted the features using principal component analysis (PCA). Discriminant analysis (DA) has been used to compare the processing effects of each feature extraction method. The experimental results have shown that the recognition rate of four gestures can reach 100.0%, the recognition rate of six gestures can reach 98.29%, and the optimal size is 516~523 dimensions. This study lays a foundation for the follow-up work of the pruning machine gesture control, and p rovides a compelling new way to promote the creative and human computer interaction process of forestry machinery.

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Estimation of continuous and constraint-free 3 DoF wrist movements from surface electromyogram signal using kernel recursive least square tracker

Cited by 18 publications

References 25 publications

Surface-EMG based Wrist Kinematics Estimation using Convolutional Neural Network

Surface-EMG based Wrist Kinematics Estimation using Convolutional Neural Network

A CNN-LSTM Hybrid Model for Wrist Kinematics Estimation Using Surface Electromyography

Exploration of Feature Extraction Methods and Dimension for sEMG Signal Classification

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