Performance Comparison of Artificial Neural Network and Gaussian Mixture Model in Classifying Hand Motions by Using sEMG Signals

Başpinar, Ulvi; Varol, H. Selçuk; Senyurek, Volkan

doi:10.1016/s0208-5216(13)70054-8

Cited by 32 publications

(13 citation statements)

References 14 publications

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“…Myocontrol is still limited to a few DOFs (Arjunan and Kumar, 2010; Yang et al, 2014), and surface electromyography (sEMG) signals are deemed to be no longer enough (Jiang et al, 2012a). Researchers have tried to address this issue by increasing the number of sensors (Tenore et al, 2007), although it is known that four to six channels are acceptable for pattern detection (Young et al, 2012), and/or to find their optimal placement given the characteristics of the stump (Castellini and van der Smagt, 2009; Fang et al, 2015); several pattern recognition algorithms have been studied, such as artificial neural networks (Baspinar et al, 2013), linear discriminant analysis (Khushaba et al, 2009) and non-linear incremental learning (Gijsberts et al, 2014). However, one of the major drawbacks of sEMG signals is their variable nature: sweat, electrode shifts, motion artifacts, ambient noise, cross-talk among deep adjacent muscles and muscular fatigue can crucially affect them (Oskoei and Hu, 2007; Cram and Kasman, 2010; Merletti et al, 2011a; Castellini et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Assessment of a Wearable Force- and Electromyography Device and Comparison of the Related Signals for Myocontrol

et al. 2016

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In the frame of assistive robotics, multi-finger prosthetic hand/wrists have recently appeared, offering an increasing level of dexterity; however, in practice their control is limited to a few hand grips and still unreliable, with the effect that pattern recognition has not yet appeared in the clinical environment. According to the scientific community, one of the keys to improve the situation is multi-modal sensing, i.e., using diverse sensor modalities to interpret the subject's intent and improve the reliability and safety of the control system in daily life activities. In this work, we first describe and test a novel wireless, wearable force- and electromyography device; through an experiment conducted on ten intact subjects, we then compare the obtained signals both qualitatively and quantitatively, highlighting their advantages and disadvantages. Our results indicate that force-myography yields signals which are more stable across time during whenever a pattern is held, than those obtained by electromyography. We speculate that fusion of the two modalities might be advantageous to improve the reliability of myocontrol in the near future.

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Section: Introductionmentioning

confidence: 99%

Assessment of a Wearable Force- and Electromyography Device and Comparison of the Related Signals for Myocontrol

et al. 2016

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“…Different hybrids of neural network were showing good performance especially to understand complex pattern from imprecise data. Research [12] proposed to control an electric powered wheelchair based on sEMG signal. The autoregressive (AR) model was proposed to extract sEMG features before using the back-propagation artificial neural network (BPANN) to classify different facial movement patterns.…”

Section: B Semg Classification and Analysis Using Computational Methodsmentioning

confidence: 99%

Single channel sEMG muscle fatigue prediction: An implementation using least square support vector machine

Sharawardi

Choo

Shao

et al. 2014

2014 4th World Congress on Information and Communication Technologies (WICT 2014)

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Surface electromyogram (sEMG) signal is commonly used for muscle fatigue analysis in clinical rehabilitation studies. Prediction results based on sEMG signals are promising because muscle contradiction can be easily characterized using sEMG signals. However, the prediction results usually deteriorate significantly when noise exist during data acquisition. Noise happens due to many factors ranging from hardware, software to procedure flaws. This investigation is aimed to assess the performance of the Least Square SVM model in predicting muscle fatigue using single channel sEMG signal. The root mean square, median frequency, and mean frequency features were extracted from two sets of raw sEMG signals captured at the multifidus (for low back pain) and flexor carpi radialis (for forearm muscle fatigue) muscles. The proposed LS-SVM technique were used to build the prediction rule-base separately for both the datasets. The implementation, testing and verification were performed in Matlab environment. The k-nearest neighbour and artificial neural network were used as the benchmarking techniques in results comparison and analysis. LS-SVM technique is proven good against the benchmarking techniques on classification accuracy and area under ROC curve. The ANOVA and Tukey HSD post hoc test were used to further validate the significant of the comparison results on both accuracy and AUC measurements.

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“…Time domain features such as Mean Absolute Value (MAV), number of Zero Crossings (ZC), number of Slope Sign Changes (SSC) and Waveform Length (WL), average EMG amplitude (aEMG), variance (VAR) and Root Mean Square (RMS) were considered for pattern recognition [35,36,37,38]. These features were extracted from 4 channels of sEMG signals during the sketching of each shape.…”

Section: Feature Extractionmentioning

confidence: 99%

Recognition of sketching from surface electromyography

Chen

Zhong

Gong

et al. 2017

Neural Comput & Applic

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The main objective of this study is to recognize sketching precisely and effectively in human computer interaction. A surface electromyography (sEMG) based sketching recognition method is proposed. We conducted an experiment in which we recorded the sEMG signals from the forearm muscles of two participants who were instructed to sketch seven basic one-stroke shapes. Subsequently, seven features of the sEMG time domain were extracted. After reducing data dimensionality using principal component analysis, these features were used as input vectors to a sketching recognition model based on Support Vector Machines (SVM). The performance of this model was compared to two other recognition models based on Multilayer perceptron (MLP) neural networks and Self Organization Feature Map (SOFM) neural networks. The average recognition rates for the seven basic one-stroke shapes of two participants achieved by the SVM-based and MLP-based models were both 98.5% in the test set, which were slightly superior to the performance of the SOFM classifier. Our results demonstrate the feasibility of converting forearm sEMG signals into sketching patterns.

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Performance Comparison of Artificial Neural Network and Gaussian Mixture Model in Classifying Hand Motions by Using sEMG Signals

Cited by 32 publications

References 14 publications

Assessment of a Wearable Force- and Electromyography Device and Comparison of the Related Signals for Myocontrol

Assessment of a Wearable Force- and Electromyography Device and Comparison of the Related Signals for Myocontrol

Single channel sEMG muscle fatigue prediction: An implementation using least square support vector machine

Recognition of sketching from surface electromyography

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