2011
DOI: 10.1007/978-3-642-21729-6_135
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Neural Network Classifier for Hand Motion Detection from EMG Signal

Abstract: EMG signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable computer, I/O for virtual reality games and physical exercise equipments. Additionally, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This paper represents the detection of different predefined hand moti… Show more

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Cited by 27 publications
(20 citation statements)
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“…The overall performance for a single trial has been found at 89.2% with an average success rate of 88.4% based on TD features as conducted by Ahsan et al [47]. Yet, the precision of ANN outputs is always limited to the least square errors as discussed in [93].…”
Section: Automated Emg Analysismentioning
confidence: 99%
“…The overall performance for a single trial has been found at 89.2% with an average success rate of 88.4% based on TD features as conducted by Ahsan et al [47]. Yet, the precision of ANN outputs is always limited to the least square errors as discussed in [93].…”
Section: Automated Emg Analysismentioning
confidence: 99%
“…In some cases, it is problematic to acquire the informative signal pattern from the residual weak muscle group of a disabled or amputee person. Even more complications may arise while dealing with the solution of multiclass classification problems [3]. EMG signals can be employed as an alternative input mechanism to control an external peripheral or device by identifying the motion commands.…”
Section: Introductionmentioning
confidence: 99%
“…Typically, a sEMG signal movement classification consists on a pattern recognition / classification algorithm, which includes several popular methods such as LDA [2,3], Artificial Neural Networks (ANN) [4,5], Fuzzy Logic [6,7], Neuro Fuzzy [8], Genetic Algorithms, Support Vector Machines [9], Bayesian Networks [10][11][12] and Logistic Regression [13]. There are also some approaches using Independent Component Analysis (ICA) [14] and Principal Component Analysis (PCA) [15,16] focusing on dimensionality reduction and efficient computation, techniques focused on provide more efficiency to classification stage.…”
Section: Introductionmentioning
confidence: 99%
“…Additionally, considering the advantages of Fuzzy Logic combined with the power of adaptation of an ANN, a Neuro-Fuzzy algorithm for myoelectric control has been proposed [1,17] for the intelligent control of a prosthesis. Also, a hierarchical Neuro-Fuzzy [7] controller has been found to be adapting well on people who generate different muscle activity levels. Recently, there have been some attempts to apply Hidden Markov Models (HMM) [11] and the Gaussian Mixture Model (GMM) [12] to upperlimb movement classification using myoelectric signals.…”
Section: Introductionmentioning
confidence: 99%