Surface-EMG based Wrist Kinematics Estimation using Convolutional Neural Network

Bao, Tianzhe; Zaidi, Ali; Xie, Shane; Zhang, Zhiqiang

doi:10.1109/bsn.2019.8771100

Cited by 25 publications

(24 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The batch normalization layer is attached to mitigate alternation made by convolutional layers [30]. As suggested in our previous work [31], the leaky ReLU layer is used in case of the dying ReLU problem [32]. The max-pooling layer (a pool size of 3 and a stride of 1) is added for sub-sampling while a dropout layer is attached for regularization.…”

Section: A Cnn-based Deep Feature Extraction 1) Construction Of Semgmentioning

confidence: 99%

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

Bao

Zaidi

Xie

et al. 2021

IEEE Trans. Instrum. Meas.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: A Cnn-based Deep Feature Extraction 1) Construction Of Semgmentioning

confidence: 99%

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

Bao

Zaidi

Xie

et al. 2021

IEEE Trans. Instrum. Meas.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The initial architecture of machine learning models was adopted from prior researches [13, 28, 33, 35–37, 55]. The number of hidden layers, neurons in each hidden layer, feedback delay, input delay, convolution filtering size, pooling size, and strides were selected by trial and error (Figure 4) to have the highest regression accuracy.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

“…Owing to the CNN’s broad feature learning capability, they have become the most popular deep learning architectures that can perform classification or regression using multi-dimensional data [30]. Lately, CNNs have been applied in sEMG-based classification of hand or wrist gestures [5, 6, 12, 17, 31, 32], and limitedly in sEMG-based regression [33, 34]. Bao et al [33] used a CNN for wrist multi-DoFs kinematics estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Lately, CNNs have been applied in sEMG-based classification of hand or wrist gestures [5, 6, 12, 17, 31, 32], and limitedly in sEMG-based regression [33, 34]. Bao et al [33] used a CNN for wrist multi-DoFs kinematics estimation. Ameri et al [35] introduced a regression-based CNN that was developed for real-time sEMG based estimation of simultaneous wrist motions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning

Nasr

Bell

et al. 2021

Preprint

View full text Add to dashboard Cite

ObjectiveThis paper proposes machine learning models for mapping surface electromyography (sEMG) signals to regression of joint angle, joint velocity, joint acceleration, joint torque, and activation torque.ApproachThe regression models, collectively known as MuscleNET, take one of four forms: ANN (Forward Artificial Neural Network), RNN (Recurrent Neural Network), CNN (Convolutional Neural Network), and RCNN (Recurrent Convolutional Neural Network). Inspired by conventional biomechanical muscle models, delayed kinematic signals were used along with sEMG signals as the machine learning model’s input; specifically, the CNN and RCNN were modeled with novel configurations for these input conditions. The models’ inputs contain either raw or filtered sEMG signals, which allowed evaluation of the filtering capabilities of the models. The models were trained using human experimental data and evaluated with different individual data.Main resultsResults were compared in terms of regression error (using the root-mean-square) and model computation delay. The results indicate that the RNN (with filtered sEMG signals) and RCNN (with raw sEMG signals) models, both with delayed kinematic data, can extract underlying motor control information (such as joint activation torque or joint angle) from sEMG signals in pick-and-place tasks. The CNNs and RCNNs were able to filter raw sEMG signals.SignificanceAll forms of MuscleNET were found to map sEMG signals within 2 ms, fast enough for real-time applications such as the control of exoskeletons or active prostheses. The RNN model with filtered sEMG and delayed kinematic signals is particularly appropriate for applications in musculoskeletal simulation and biomechatronic device control.

show abstract

“…For the continuous estimation of the joint angle, there are various methods. Bao et al [18] presented a single stream convolutional neural network (CNN) for mapping sEMG to wrist angles within three degrees-of-freedoms. Xiao et al [19] used the mean absolute value, waveform length, zero crossing, slope signs changes, and the difference in absolute standard deviation value of sEMG, in order to estimate continuous elbow motion by random forest (RF).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis

Guan

Zou

et al. 2019

Electronics

View full text Add to dashboard Cite

To study the relationship between surface electromyography (sEMG) and joint movement, and to provide reliable reference information for the exoskeleton control, the sEMG and the corresponding movement of the knee during the normal walking of adults have been measured. After processing the experimental data, the estimation model for knee movement from sEMG was established using the novel method of random forest with principal component analysis (RFPCA). The influence of the sample size and the previous sEMG data on the prediction efficiency was analyzed. The estimation model was not sensitive to the sample size when samples increased to a certain value, and the results of different previous sEMG showed that the prediction accuracy of the estimation models did not always improve with the increasing features of input. By comparing the estimation model of back propagation neural network with principal component analysis (BPPCA), it was found that RFPCA was suitable for all participants in the experiment with less execution time, and the root mean square error was around 5 • which was lower than BPPCA with errors varying from 7 • to 25 • . Therefore, it was concluded that the RFPCA method for the estimation of knee movement from sEMG is feasible and could be used for motion analysis and the control of exoskeleton.

show abstract

Surface-EMG based Wrist Kinematics Estimation using Convolutional Neural Network

Cited by 25 publications

References 16 publications

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

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

MuscleNET: mapping electromyography to kinematic and dynamic biomechanical variables by machine learning

Estimation of Knee Movement from Surface EMG Using Random Forest with Principal Component Analysis

Contact Info

Product

Resources

About