A Method of Recognizing Finger Motion Using Wavelet Transform of Surface EMG Signal

Jiang, Mengqing; Wang, R.C.; Wang, J.Z.; Jin, Dewen

doi:10.1109/iembs.2005.1617020

Cited by 55 publications

(30 citation statements)

References 6 publications

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“…Different sEMG-based systems were proposed for the estimation of hand and wrist movements, and consequently used as noninvasive interfaces for controlling exoskeletons [5,6], prosthetic devices [7][8][9], computer-animated hands in a virtual environment [10], or for teleoperating robotic arms [9,11]. The previous studies focused on the investigation of discrete classifications of wrist abduction/adduction [9,11], flexion/extension [7,10,12,13] as well as of a different combination of finger motions [9,11,14]. Such strategies are useful for accomplishing power grasps that require simultaneous closure of all fingers on the object.…”

Section: Introductionmentioning

confidence: 99%

EMG-based decoding of grasp gestures in reaching-to-grasping motions

Batzianoulis

El-Khoury

Pirondini

et al. 2017

Robotics and Autonomous Systems

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Section: Introductionmentioning

confidence: 99%

EMG-based decoding of grasp gestures in reaching-to-grasping motions

Batzianoulis

El-Khoury

Pirondini

et al. 2017

Robotics and Autonomous Systems

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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%

“…Over the past decades, different electrode placement strategies have been investigated. Some researchers study the use of multichannel electrode arrays [20] or high-density EMG (large number of electrodes) strategy [21,22], while others explore the precise anatomical positioning approach [18,23].…”

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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“…The two frequency-domain features were median frequency and mean power frequency [16][17][18]. The twelve time-and frequency-domain features included maximum, singular value, average energy, VAR, standard deviation, and WL of wavelet coefficients and wavelet packet coefficients [19][20][21][22][23][24][25][26]. The three nonlinear dynamic features were entropy of wavelet coefficients, entropy of wavelet packet coefficients, and maximum of Lyapunov exponent [27][28][29].…”

Section: Feature Set Computation and Reductionmentioning

confidence: 99%

Classification of surface electromyogram signals based on directed acyclic graphs and support vector machines

Hu¹,

Kan²,

Li³

2018

Turk J Elec Eng & Comp Sci

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This paper presents a novel classification approach for surface electromyogram (sEMG) signals. The proposed classification approach involves two steps: (1) feature extraction from an sEMG, in which a 7-dimensional feature vector is extracted from 27 types of features of the sEMG by linear discriminant analysis (LDA), and (2) a novel classifier, DAGSVMerr, based on a directed acyclic graph (DAG) and support vector machine (SVM), in which a separability measure function based on erroneous recognition rates (ERRs) is defined to determine the initial operation list. The proposed approach takes advantage of the feedback idea to improve the performance of the classification. The experimental results show that the proposed approach has a better performance than traditional methods, and it achieves an average classification accuracy rate of 99.4% ± 1.3% with an error rate of 0.6%. Correct classification rates of the proposed approach are very high, and the approach can be utilized to recognize gesture instructions by analyzing sEMG signals in gesture equipment control studies.

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A Method of Recognizing Finger Motion Using Wavelet Transform of Surface EMG Signal

Cited by 55 publications

References 6 publications

EMG-based decoding of grasp gestures in reaching-to-grasping motions

EMG-based decoding of grasp gestures in reaching-to-grasping motions

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

Classification of surface electromyogram signals based on directed acyclic graphs and support vector machines

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