Hand Sign Classification Employing Myoelectric Signals of Forearm

Tsujimura, Tohru; Yamamoto, Sho; Izumi, Kiyotaka

doi:10.5772/51080

Cited by 11 publications

(8 citation statements)

References 13 publications

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“…The sampled frequency most commonly used is 1 kHz [1,9,10,12,13,18,19,[27][28][29]. Other authors (e.g., [2,11,22,26,30,31]) use a higher frequency of 1.5, 2, 3, 4, or 10 kHz. In addition, some authors use lower frequencies, such as 500 Hz [15,32].…”

Section: Referencementioning

confidence: 99%

“…Sampling Frequency [15,[32][33][34] 500 Hz [1,9,10,[12][13][14][17][18][19][20][21][27][28][29] 1 kHz [2] 1.5 kHz [7,8,11,16,30,[35][36][37] 2 kHz [22] 3 kHz [6,25,26] 4 kHz [31] 10 kHz…”

Section: Referencementioning

confidence: 99%

“…[25] 1 [16,17,20,26] 2 [24,31] 3 [1,[8][9][10]13,21,23,29,35,39,[44][45][46] 4 [19,36,40,41] 6 [2,7,11,15,22,30,32,34] 8 [37] 12 [33] 14 [12,14] 16 [47] 22 Table 4. Electrodes type and place of electrode placement body.…”

Section: Number Of Channelsmentioning

confidence: 99%

“…Electrode Type and Body Region [5,14,16,18,19,37,39,40,[48][49][50] Monopolar and Upper limb [2,12,26,51] Monopolar and Lower limb [36,47] Monopolar and Facial muscles [4,6,[9][10][11]15,20,22,24,27,29,31,32,34,35,44,[52][53][54] Bipolar and Upper limb [13,30] Bipolar and Lower limb [45] Bipolar and Cheek [7] Bipolar and Facial muscles…”

Section: Referencementioning

confidence: 99%

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Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

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This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

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Section: Referencementioning

confidence: 99%

“…Sampling Frequency [15,[32][33][34] 500 Hz [1,9,10,[12][13][14][17][18][19][20][21][27][28][29] 1 kHz [2] 1.5 kHz [7,8,11,16,30,[35][36][37] 2 kHz [22] 3 kHz [6,25,26] 4 kHz [31] 10 kHz…”

Section: Referencementioning

confidence: 99%

Section: Number Of Channelsmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

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Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

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“…As can be seen, the number of electrode is greater than or equal to the number of classes in nearly all of these studies. Also, almost all of the current works only deals with small number of classes (less than 10) [11,[17][18][19][20]23,29,30]. Only an extremely small number of studies have reported classification results for more than 10 classes but they require large number of electrodes [21,[31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand

et al. 2017

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Featured Application: This work is intended for the development of myoelectric prosthetic hand systems. Furthermore, the outcome of this study may also benefit other electromyographic based human-machine interfaces. Abstract:The myoelectric prosthetic hand is a powerful tool developed to help people with upper limb loss restore the functions of a biological hand. Recognizing multiple hand motions from only a few electromyography (EMG) sensors is one of the requirements for the development of prosthetic hands with high level of usability. This task is highly challenging because both classification rate and misclassification rate worsen with additional hand motions. This paper presents a signal processing technique that uses spectral features and an artificial neural network to classify 17 voluntary movements from EMG signals. The main highlight will be on the use of a small set of low-cost EMG sensor for classification of a reasonably large number of hand movements. The aim of this work is to extend the capabilities to recognize and produce multiple movements beyond what is currently feasible. This work will also show and discuss about how tailoring the number of hand motions for a specific task can help develop a more reliable prosthetic hand system. Online classification experiments have been conducted on seven male and five female participants to evaluate the validity of the proposed method. The proposed algorithm achieves an overall correct classification rate of up to 83%, thus, demonstrating the potential to classify 17 movements from 6 EMG sensors. Furthermore, classifying 9 motions using this method could achieve an accuracy of up to 92%. These results show that if the prosthetic hand is intended for a specific task, limiting the number of motions can significantly increase the performance and usability.

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Surface Electromyography Time Series Analysis for Regaining the Intuitive Grasping Capability After Thumb Amputation

Kumarasinghe

Withanage

2014

Communications in Computer and Information Science

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Hand Sign Classification Employing Myoelectric Signals of Forearm

Cited by 11 publications

References 13 publications

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand

Surface Electromyography Time Series Analysis for Regaining the Intuitive Grasping Capability After Thumb Amputation

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