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Real-time estimation of tongue movement based on suprahyoid muscle activity. 2013

M Sasaki, and K Onishi, and T Arakawa, and A Nakayama, and D Stefanov, and M Yamaguchi

In this study, we introduce a real-time method for tongue movement estimation based on the analysis of the surface electromyography (EMG) signals from the suprahyoid muscles, which usual function is to open the mouth and to control the position of the hyoid, the base of the tongue. Nine surface electrodes were affixed to the underside of the jaw and their signals were processed via multi-channel EMG system. The features of the EMG signals were extracted by using a root mean square (RMS) method. The dimension of the variables was reduced additionally from 108 to 10 by applying the Principal Component Analysis (PCA). The feature quantities of the reduced dimension set were associated with the tongue movements by using an artificial neural network. Results showed that the proposed method allows precise estimation of the tongue movements. For the test data set, the identification rate was greater than 97 % and the response time was less than 0.7 s. The proposed method could be implemented to facilitate novel approaches for alternative communication and control of assistive technology for supporting the independent living of people with severe quadriplegia.

UI MeSH Term Description Entries
D008297 Male Males
D009068 Movement The act, process, or result of passing from one place or position to another. It differs from LOCOMOTION in that locomotion is restricted to the passing of the whole body from one place to another, while movement encompasses both locomotion but also a change of the position of the whole body or any of its parts. Movement may be used with reference to humans, vertebrate and invertebrate animals, and microorganisms. Differentiate also from MOTOR ACTIVITY, movement associated with behavior. Movements
D009334 Neck Muscles The neck muscles consist of the platysma, splenius cervicis, sternocleidomastoid(eus), longus colli, the anterior, medius, and posterior scalenes, digastric(us), stylohyoid(eus), mylohyoid(eus), geniohyoid(eus), sternohyoid(eus), omohyoid(eus), sternothyroid(eus), and thyrohyoid(eus). Muscle, Neck,Muscles, Neck,Neck Muscle
D004566 Electrodes Electric conductors through which electric currents enter or leave a medium, whether it be an electrolytic solution, solid, molten mass, gas, or vacuum. Anode,Anode Materials,Cathode,Cathode Materials,Anode Material,Anodes,Cathode Material,Cathodes,Electrode,Material, Anode,Material, Cathode
D004576 Electromyography Recording of the changes in electric potential of muscle by means of surface or needle electrodes. Electromyogram,Surface Electromyography,Electromyograms,Electromyographies,Electromyographies, Surface,Electromyography, Surface,Surface Electromyographies
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465 Algorithms A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task. Algorithm
D012815 Signal Processing, Computer-Assisted Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity. Digital Signal Processing,Signal Interpretation, Computer-Assisted,Signal Processing, Digital,Computer-Assisted Signal Interpretation,Computer-Assisted Signal Interpretations,Computer-Assisted Signal Processing,Interpretation, Computer-Assisted Signal,Interpretations, Computer-Assisted Signal,Signal Interpretation, Computer Assisted,Signal Interpretations, Computer-Assisted,Signal Processing, Computer Assisted
D014059 Tongue A muscular organ in the mouth that is covered with pink tissue called mucosa, tiny bumps called papillae, and thousands of taste buds. The tongue is anchored to the mouth and is vital for chewing, swallowing, and for speech. Tongues
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron

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