A Real-Time Capable Linear Time Classifier Scheme for Anticipated Hand Movements Recognition from Amputee Subjects Using Surface EMG Signals

Arunraj, M.; Srinivasan, Andy; Arjunan, Sridhar P.

doi:10.1016/j.irbm.2020.08.003

Cited by 16 publications

(7 citation statements)

References 33 publications

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“…The authors of the NinaPro database presented a performance baseline for 50 hand movements in 11 amputees [8,27], where the average accuracy with a CNN model was below 40% and was 42.67% with a SVM classifier. Arunraj et al [39] also computed the average performance with 53.3% for 50 movements in 11 amputees. They selected the Logarithmic Variance (LV), Auto-Regressive Co-efficient (ARC), WL and MAV with the Random Fourier Mapping (RFM) method to classify hand movements.…”

Section: Discussionmentioning

confidence: 99%

Improving sEMG-based motion intention recognition for upper-limb amputees using transfer learning

Fan

Jiang

Chen

et al. 2021

Neural Comput & Applic

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Hand gesture recognition from multi-channel surface electromyography (sEMG) have been widely studied in the past decade. By analyzing muscle activities measured from forearm muscles, multiple hand gestures can be recognized. This technology can benefit upper-limb amputees in motion intention recognition, especially for those with trans-radial amputation, in terms of prosthesis control, rehabilitation and further human-computer interaction. However, due to the scarcity of signals collected from amputees, many related studies used signals from intact subjects as a proxy and result in overoptimistic classification performance. Comparing to sEMG signals from intact subjects, signals from upper-limb amputees suffer from signal quality deterioration which relates to the level of amputation and maybe other amputation information. Therefore, this study aims at improving the motion intention recognition performance in trans-radial amputated subjects. To tackle the challenges of data scarcity and signal quality deterioration, we propose a CNN-based transfer learning solution leveraging the knowledge learned from sEMG signals of intact subjects. The proposed method was developed from and tested with NinaPro database where 20 intact subjects and 11 amputees. We obtained 67.5% accuracy in the mDWT feature after transfer. And the results improved by 9.4% after transfer compared to no transfer in the RMS feature. In the end of the study, we further discussed the correlation between classification accuracy and amputation information including the percentage of remaining forearm and the number of years since amputation.

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Section: Discussionmentioning

confidence: 99%

Improving sEMG-based motion intention recognition for upper-limb amputees using transfer learning

Fan

Jiang

Chen

et al. 2021

Neural Comput & Applic

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“…Hence, features were extracted using feature extraction methods (i.e., FS1, FS2, FS3, and FS4) that created a high-dimensional feature space, as mentioned in Section 2.2.2. e feature dimensionality was reduced (total classes − 1 � 10 − 1 � 9) using ULDA [73]. Now, the 9-dimensional reduced feature vectors for each feature extraction method were classified using three popular classifiers: LDA with quadratic function [74,75], SVM with Gaussian radian basis kernel function (sigma value � 1) [76], and KNN with cityblock distance (neighbours � 3) [37]. In this performance evaluation, five trials containing 1000 samples (trials × movements × samples per trial � 5 × 10 × 20) were used as training data, and the remaining trial containing 200 samples (trials × movements × samples per trial � 1 × 10 × 20) was used as testing data.…”

Section: Emg Pattern Recognition Methodmentioning

confidence: 99%

Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features

Islam

Ahmad

Haque

et al. 2022

Computational Intelligence and Neuroscience

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The multichannel electrode array used for electromyogram (EMG) pattern recognition provides good performance, but it has a high cost, is computationally expensive, and is inconvenient to wear. Therefore, researchers try to use as few channels as possible while maintaining improved pattern recognition performance. However, minimizing the number of channels affects the performance due to the least separable margin among the movements possessing weak signal strengths. To meet these challenges, two time-domain features based on nonlinear scaling, the log of the mean absolute value (LMAV) and the nonlinear scaled value (NSV), are proposed. In this study, we validate the proposed features on two datasets, the existing four feature extraction methods, variable window size, and various signal-to-noise ratios (SNR). In addition, we also propose a feature extraction method where the LMAV and NSV are grouped with the existing 11 time-domain features. The proposed feature extraction method enhances accuracy, sensitivity, specificity, precision, and F1 score by 1.00%, 5.01%, 0.55%, 4.71%, and 5.06% for dataset 1, and 1.18%, 5.90%, 0.66%, 5.63%, and 6.04% for dataset 2, respectively. Therefore, the experimental results strongly suggest the proposed feature extraction method, for taking a step forward with regard to improved myoelectric pattern recognition performance.

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“…To this end, the raw EEG signal was passed through a preprocessing step for feature extraction. Several feature selection methods have been proposed in the literature; wavelet transforms, eigenvectors, time-frequency distributions, autoregressive models to name a few [12]- [14]. In this research, the two methods of feature extraction are investigated, one involving the raw EEG data and the other involving Fourier transformed EEG data.…”

Section: Motion Estimationmentioning

confidence: 99%

Estimation of the Shoulder Joint Angle using Brainwaves

Sasaki

Iida

Muguro

et al. 2021

AJEEET

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This paper presents the angle of the shoulder joint as basic research for developing a machine interface using EEG. The raw EEG voltage signals and power density spectrum of the voltage value were used as the learning feature. Hebbian learning was used on a multilayer perceptron network for pattern classification for the estimation of joint angles 0o, 90o and 180o of the shoulder joint. Experimental results showed that it was possible to correctly classify up to 63.3% of motion using voltage values of the raw EEG signal with the neural network. Further, with selected electrodes and power density spectrum features, accuracy rose to 93.3% with more stable motion estimation.

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A Real-Time Capable Linear Time Classifier Scheme for Anticipated Hand Movements Recognition from Amputee Subjects Using Surface EMG Signals

Cited by 16 publications

References 33 publications

Improving sEMG-based motion intention recognition for upper-limb amputees using transfer learning

Improving sEMG-based motion intention recognition for upper-limb amputees using transfer learning

Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features

Estimation of the Shoulder Joint Angle using Brainwaves

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