Wrist EMG Pattern Recognition System by Neural Networks and Multiple Principal Component Analysis

Matsumura, Yuji; Fukumi, Minoru; Akamatsu, Norio; Takeda, Fumiaki

doi:10.1007/978-3-540-30132-5_120

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Considering the relevance and redundancy between the features, to reduce the computing complexity, the feature dimensionality reduction is of great necessity. One popular method is the principal components analysis (PCA) [26,27] . In this paper, the dimensionality of the original feature vector is 16.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

Motion classification of EMG signals based on wavelet packet transform and LS-SVMs ensemble

Yan

You

Chen

et al. 2009

Trans. Tianjin Univ.

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Abstract：This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification. Keywords：pattern recognition; wavelet packet transform; least squares support vector machine; surface electromyographic signal; neural network; separability Recently, bio-signals have been paid much attention due to their potentiality to provide convenient control channels between the disabled men and rehabilitation engineering [1][2][3] . By analyzing the electromyographic (EMG) signals collected from remnant muscles, as control inputs for the artificial limb, the intelligent artificial limbs can implement the corresponding functions. Recognizing bio-signals correctly is a preliminary stage of controlling the prosthesis devices. Various methods of determining features of bio-signals were known for discerning predefined motions of human [4][5][6][7] . In this paper, what is concerned about is the motion classification of the forearm by studying the corresponding surface electromyographic (sEMG) signals. The EMG signal is a form of electric manifestation of neuromuscular activation associated with contracting muscles and there exist many factors affecting its waveform and amplitude [8] , which makes it very difficult to be analyzed.There are various methods to identify the motion modes via EMG signals for assistive devices such as powered prosthesis [1,8,9] . In the case of EMG classification, many efforts from two aspects have been made to seek the efficient and effective classification strategy. In the first category [7,9] , attempts are made to find a certain set of features with good separability to the predefined classes.In this category, the problem gets straightforward once one finds appropriate threshold values of features, which make the least overlapping among the predefined classes to be classified. Nevertheless, under many circumstances, the feature extraction is a subject-dependent problem [7] . An effective set of features used in one circumstance maybe does not work well when used in another circumstance. In the other category in Refs. [1,6,10,11], the researchers employed some specific classifiers with advisable features to cope with the classification task. This category includes the methods of using the probabilistic model, the fuzzy classifier, and the neural network classifier. In these methods, the errors in the boundaries often can...

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Section: Feature Extraction and Selectionmentioning

confidence: 99%

Motion classification of EMG signals based on wavelet packet transform and LS-SVMs ensemble

Yan

You

Chen

et al. 2009

Trans. Tianjin Univ.

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“…Soft‐computing techniques can be performed separately or jointly to assess the relationship between EMG signals and kinetics/kinematics variables (Brzostowski, ; Hou et al., ; Hou et al., ; Karwowski et al., ; Lee et al., ; Liu and Young, , ). In addition to EMG modeling, soft‐computing models have been applied by several authors to classify complicated EMG patterns such as hand motions (Karimi, Pourghassem, & Shahgholian, ; Karlik, Tokhi, & Alci, ; Khezri & Jahed, , ; Khushaba & Al‐Jumaily, ; Matsumura, Fukumi, & Akamatsu, ; Oskoei & Hu, ; Shi, Cai, Zhu, Zhong, & Wang, ; Wang, Yan, Hu, Xie, & Wang, ; Yan, Wang, & Xie, ; Zalzala & Chaiyaratana ; Zhang, Yang, Xu, & Zhang, ), wrist motions (Qingju & Kai, ; Tohi, Mitsukura, Yazama, & Fukumi, ; Yazama, Fukumi, Mitsukura, & Akamatsu, ), leg motions (Hussein & Granat, ), arm motions (Balbinot & Favieiro, ; Micera, Sabatini, Dario, & Rossi, ; Micera, Sabatini, & Dario, ), and finger motions (Kanitz, Antfolk, Cipriani, Sebelius, & Carrozza, ).…”

Section: Introductionmentioning

confidence: 99%

Estimating electromyography responses using an adaptive neuro‐fuzzy inference system with subtractive clustering

Çakıt

Karwowski

2017

Hum Ftrs & Erg Mfg Svc

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This study aimed to develop an adaptive neuro‐fuzzy inference system (ANFIS) approach to estimate the normalized electromyography (NEMG) responses, where the independent variables are demographic variables including population, gender, ethnicity, age, height, weight, posture, and muscle groups. The study groups comprised 75 US‐based (54 males and 21 females) and 10 Japan‐based (all males) automobile assembly workers. A total of 65 inputs and 1 output reflecting the NEMG values were considered at the beginning. After correlating analysis results, a total of 35 significant predictors were considered for both ANFIS and regression models. The data were partitioned into two datasets, one for training (70% of all data) and one for validation (30% of all data). In addition to a soft‐computing approach, a multiple linear regression (MLR) analysis was also performed to evaluate whether or not the ANFIS approach showed superior predictive performance compared to a classical statistical approach. According to the performance comparison, ANFIS had better predictive accuracy than MLR, as demonstrated by the experimental results. Overall, this study demonstrates that the ANFIS approach can predict normalized EMG responses according to subjects’ demographic variables, posture, and muscle groups.

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“…Their results suggested that the classification accuracy of the hybrid RBF-MLP network was approximately 6% higher than a MLP in the classification four-class EMG signals. Matsumura et al [ 78 ] aimed at constructing a high-speed and high-accuracy EMG recognition system with fast Fourier transform (FFT) for feature extraction, simple-PCA for feature compression, and ANN for recognition. In the meantime, they reduced the node number in the input layer of the ANN using GA optimization to improve training and testing efficiency.…”

Section: Introductionmentioning

confidence: 99%

Hybrid soft computing systems for electromyographic signals analysis: a review

Xie

Guo

Bai

et al. 2014

BioMedical Engineering OnLine

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Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.

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Wrist EMG Pattern Recognition System by Neural Networks and Multiple Principal Component Analysis

Cited by 6 publications

References 2 publications

Motion classification of EMG signals based on wavelet packet transform and LS-SVMs ensemble

Motion classification of EMG signals based on wavelet packet transform and LS-SVMs ensemble

Estimating electromyography responses using an adaptive neuro‐fuzzy inference system with subtractive clustering

Hybrid soft computing systems for electromyographic signals analysis: a review

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