Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience

Shim, Hyeon-Min; Lee, Sang‐Min

doi:10.1007/s11771-015-2698-0

Cited by 42 publications

(32 citation statements)

References 17 publications

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“…For EMG pattern recognition, DBN has been used to replace conventional machine learning approaches to discriminate a five-wrist-motion problem using hand-crafted time domain features [24]. The results showed that DBN yields a better classification accuracy than LDA, SVM, and MLP, but that the DBN requires lengthy iterations to attain good performance in recognizing EMG patterns without overfitting.…”

Section: Unsupervised Pre-trained Network (Upns)mentioning

confidence: 99%

“…This is, at least in part, due to the lack of sufficient EMG data availability to train these deep neural networks in the earlier years of the field. With the advent of shared bigger EMG data sets and recent advances in techniques for addressing overfitting problems, most emerging deep learning architectures and methods have now been employed in EMG pattern recognition systems (e.g., [14,23,24]). In some cases, both feature engineering and learning are combined by inputing pre-processed data or pre-extracted features to a deep learning algorithm with some benefits having been shown (e.g., references [11,23,24]).…”

Section: Introductionmentioning

confidence: 99%

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EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

200

105

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Abstract:The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle "big data". Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on "deep learning". Finally, directions for future research in EMG pattern recognition are outlined and discussed.

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Section: Unsupervised Pre-trained Network (Upns)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

200

105

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“…To recognize the wrist posture, the EMG of the flexor carpi ulnaris (FCU) and extensor carpi ulnaris (ECU) were recorded. In our previous study, a Gaussian mixture model (GMM) and a deep belief network (DBN) were applied [22,23]. DBN has been shown to yield a better accuracy than GMM, linear discriminant analysis (LDA), or SVM.…”

Section: Pattern Recognition Processmentioning

confidence: 99%

“…In this work, EMG signal data from our previous work are used [22,23]. The EMG signals from five wrist motions-named up, down, left, right, and rest-were acquired.…”

Section: Participants and Emg Signal Acquisitionmentioning

confidence: 99%

EMG Pattern Classification by Split and Merge Deep Belief Network

Shim

Lee

et al. 2016

Symmetry

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Abstract:In this paper; we introduce an enhanced electromyography (EMG) pattern recognition algorithm based on a split-and-merge deep belief network (SM-DBN). Generally, it is difficult to classify the EMG features because the EMG signal has nonlinear and time-varying characteristics. Therefore, various machine-learning methods have been applied in several previously published studies. A DBN is a fast greedy learning algorithm that can identify a fairly good set of weights rapidly-even in deep networks with a large number of parameters and many hidden layers. To reduce overfitting and to enhance performance, the adopted optimization method was based on genetic algorithms (GA). As a result, the performance of the SM-DBN was 12.06% higher than conventional DBN. Additionally, SM-DBN results in a short convergence time, thereby reducing the training epoch. It is thus efficient in reducing the risk of overfitting. It is verified that the optimization was improved using GA.

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“…However, they did not give their result on the Movie dataset. To data, deep learning (DL) has been demonstrated as an effective model in computer science [3,6,9,16,20], and the state-of-the-art results have been promoted extensively in many fields, including face recognition. Ding et al [6] constructed a Convolutional Neural Network (CNN) that contained nine layers, including the input and output layers.…”

Section: Introductionmentioning

confidence: 99%

Violent scene detection algorithm based on kernel extreme learning machine and three-dimensional histograms of gradient orientation

Song

Zhou

et al. 2018

Multimed Tools Appl

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Most existing feature descriptors for video have limited representation ability. In order to improve the recognition accuracy of method for detecting the videos that include violent scenes and take advantage of the logical structure of video sequences, a novel feature constructing approach based on three dimensional histograms of gradient orientation (HOG3D), the Bag of Visual Words (BoVW) model, and feature pooling technology is proposed. This approach, combined with kernel extreme learning machine (KELM), can be used to detect violent scene. First, the HOG3D feature is extracted on the block level for video, and then the K-Means clustering algorithm is implemented to generate visual words. Then, the bag of visual words framework is used for the quantization of feature. And the feature pooling technology is operated to generate a feature vector for an entire video segment, and feature vectors of training data and testing data were used separately to train the model and evaluate the performance of the proposed approach. The experimental results showed that the proposed feature descriptor had good representation and generalization abilities. The proposed approach is efficient for violent scene detection, and the accuracy matches the best result on Hockey dataset, and it outperforms state-of-the-art on Movies.

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Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience

Cited by 42 publications

References 17 publications

EMG Pattern Recognition in the Era of Big Data and Deep Learning

EMG Pattern Recognition in the Era of Big Data and Deep Learning

EMG Pattern Classification by Split and Merge Deep Belief Network

Violent scene detection algorithm based on kernel extreme learning machine and three-dimensional histograms of gradient orientation

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