Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach

Jia, Guangyu; Lam, Hak‐Keung; Ma, Shugen; Yang, Zhaohui; Xu, Yujia; Xiao, Bo

doi:10.1109/tnsre.2020.2986884

Cited by 47 publications

(28 citation statements)

References 40 publications

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“…For that reason, three different levels of Gaussian noise are added to the raw EMG data to contaminate it and then the contaminated data was fed to the classifiers. Since raw inputs' given order of magnitude is 10 −5 , we set the noise levels accordingly to 1 × 10 −5 , 2 × 10 −5 , and 1 × 10 −4 (Jia, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics

Zhou

Zhang

et al. 2021

Front. Neurorobot.

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Wearable hand robots are becoming an attractive means in the facilitating of assistance with daily living and hand rehabilitation exercises for patients after stroke. Pattern recognition is a crucial step toward the development of wearable hand robots. Electromyography (EMG) is a commonly used biological signal for hand pattern recognition. However, the EMG based pattern recognition performance in assistive and rehabilitation robotics post stroke remains unsatisfactory. Moreover, low cost kinematic sensors such as Leap Motion is recently used for pattern recognition in various applications. This study proposes feature fusion and decision fusion method that combines EMG features and kinematic features for hand pattern recognition toward application in upper limb assistive and rehabilitation robotics. Ten normal subjects and five post stroke patients participating in the experiments were tested with eight hand patterns of daily activities while EMG and kinematics were recorded simultaneously. Results showed that average hand pattern recognition accuracy for post stroke patients was 83% for EMG features only, 84.71% for kinematic features only, 96.43% for feature fusion of EMG and kinematics, 91.18% for decision fusion of EMG and kinematics. The feature fusion and decision fusion was robust as three different levels of noise was given to the classifiers resulting in small decrease of classification accuracy. Different channel combination comparisons showed the fusion classifiers would be robust despite failure of specific EMG channels which means that the system has promising potential in the field of assistive and rehabilitation robotics. Future work will be conducted with real-time pattern classification on stroke survivors.

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Section: Methodsmentioning

confidence: 99%

Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics

Zhou

Zhang

et al. 2021

Front. Neurorobot.

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“…The SVM can deal with linearly separable data and not linearly separable. Since this work deals with not linearly separable because it maps the data to a higher dimension and uses the radial basis function (RBF) to obtain a high classification rate [26,27]. Therefore, the RBF is considered as the kernel function to generate non-linear classifiers.…”

Section: Classification Methodsmentioning

confidence: 99%

“…Fine KNN Classifier is type of KNN that makes it finely itemized to distinguish among different classes with the number of neighbours set to 1 [25,26].…”

Section: K-nearest-neighbour's (Knn)mentioning

confidence: 99%

EMG Signals Classification of Wide Range Motion Signals for Prosthetic Hand Control

Mahmood¹

2021

IJIES

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The recent revolution in the biomedical field carried out the researchers to work on the prosthetic technique because it reflects the amputee's need. Therefore, the electromyography (EMG) signals generated by muscle contractions are used to implement the prosthetic human body parts. This paper presents a pattern recognition system based on two EMG data; the first EMG data represents the general body movements collected from biceps and triceps muscles for six different motions, i.e., bowing, clapping, handshaking, hugging, jumping, and running. The Root Mean Square, Difference Absolute Standard Deviation Value, and Principal Component Analysis extract the raw signal data features and enhance classification accuracy. The machine learning method is applied, i.e., Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) are used for data classification; the results show high accuracy reached 94.8% and 98.9%, respectively. Whereas, the second EMG data is selected to be more specific in hand movements, i.e., cylindrical, spherical, palmar, lateral, hook, and tip motions, because these significant motions are the first step implementing any prosthetic hand. Consequently, the mean, Standard Deviation Value, and Principal Component Analysis extract the raw signal feature. Meanwhile, the same algorithm used in the first data classification is also used to classify the second data because it shows high accuracy and good performance. SVM algorithm is used to classify the data and achieved high training accuracy, reaching 89%. The high training accuracy for different hand movements is considered an essential step toward implementing human prosthetic parts to help the people who suffer from an amputee.

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“…In order to enhance the practicality and usability of the proposed approach, we investigate how to reduce the number of (a) training gestures and (b) sensors to find the optimal selection while yielding similar performance to the The intuition of gesture-based optimization of the input space comes from synergistic similarities among sEMG signals of different gestures that can be clustered into lowdimensional finite groups [30], from each of which one representative gesture can be selected as a training gesture. The sensor-based optimization of the input space is based on individual sensor performance ranking.…”

Section: B Gesture and Sensor Selectionmentioning

confidence: 99%

X-MyoNET: Biometric Identification using Deep Processing of Transient Surface Electromyography

Sarmadi

Gulati

et al. 2021

Preprint

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The rapid development of the Internet and various applications such as the Internet of Medical Things (IoMT) has raised substantial concerns about personal information security. Conventional methods (e.g., passwords) and classic biological features (e.g., fingerprints) are security deficient because of potential information leakage and hacking. Biometrics that express behavioral features suggest a robust approach to achieving information security because of the corresponding uniqueness and complexity. In this paper, we consider identifying human subjects based on their transient neurophysiological signature captured using multichannel upper-limb surface electromyography (sEMG). An explainable artificial intelligence (XAI) approach is proposed to process the internal dynamics of temporal sEMG signals. We propose and prove the suitability of “transient sEMG” as a biomarker that can identify individuals. For this, we utilize the Gradient-weighted Class Activation Mapping (Grad-CAM) analysis to explain the network’s attention. The outcome not only decodes and makes the unique neurophysiological pattern (i.e., motor unit recruitment during the transient phase of contraction) associated with each individual visualizable but also generates an optimizing two-dimensional (2D) spectrotemporal mask used to significantly reduce the size of the model and the trainable parameters. The resulting mask selectively and systematically samples the spectrotemporal characteristics of the users’ neurophysiological responses, discarding 40% of the input space while securing the accuracy of about 74% with much shallower neural network architecture. In the systematic comparative study, we find that our proposed model outperforms several state-of-the-art algorithms. For broader impacts, we anticipate our design of a compact, practical, interpretable, and robust identification system that requires only a minimal number of gestures and sensors (only 7% of the entire data set) to be a starting point for small and portable identification hardware.

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Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach

Cited by 47 publications

References 40 publications

Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics

Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics

EMG Signals Classification of Wide Range Motion Signals for Prosthetic Hand Control

X-MyoNET: Biometric Identification using Deep Processing of Transient Surface Electromyography

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