Volume 14, Number 1 (6-2017)                   JSDP 2017, 14(1): 41-52 | Back to browse issues page



DOI: 10.18869/acadpub.jsdp.14.1.41

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Goharian N, Moghimi S, Kalani H. Application of an ANN-GA Method for Predicting the Biting Force Using Electromyogram Signals. JSDP. 2017; 14 (1) :41-52
URL: http://jsdp.rcisp.ac.ir/article-1-361-en.html

Assistant Professor Ferdowsi University of Mashhad
Abstract:   (246 Views)

Human mastication is a common rhythmic behavior and a complex biomechanical process which is hard to reproduce. Today, investigating the relation between electrical activity of muscles and force signals is of high importance in many applications including gait analysis, orthopedics, rehabilitation, ergonomic design, haptic technology, tele-presence surgery and human-machine interaction. Surface electrodes have many advantages over force sensors which are often expensive and of massive structure, two of which are less expensive and portable. Since the biting force is too difficult to be measured, in this paper, we aim to investigate the ability of a Multi-Layer Perceptron artificial neural network (MLPANN) and Radial Basis Function artificial neural network (RBFANN) to predict the biting force of incisor teeth based on surface electromyography (EMG) signals. RBFANN and MLPANN are two of the most widely used neural network architecture. These two methods are both known as universal approximates for nonlinear input-output mapping. To do this, biting force and EMG signals from the masticatory muscles were recorded and used as output and input of neural networks, respectively. Genetic algorithm was applied to find the best structure for ANNs and the appropriate total time-delay of EMGs. Results show that the EMG signals recorded from aforementioned muscles contain useful information about the biting force. Furthermore, they indicate that MLPANN and RBFANN can detect the dynamics of the system with good precision. The mean percentage error in the training and validation phase is %2.3 and %19.4 for MLPANN and %8.3 and %22.7 for RBFANN, sequentially. Also the variance analysis technique shows that there is no significant difference between results achieved through MLPANN and RBFANN. The provided analysis will aid researchers in characterizing and investigating the mastication process, through the specification of SEMG signal patterns and the observation of the resulting biting force. Such models can provide clinical insight into the development of more effective rehabilitation therapies, and can aid in assessing the effects of an intervention. This methodology can be applied to any tele-operated robot or orthotic device (exoskeleton), either for rehabilitation or extension of human ability.

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Type of Study: Research | Subject: Paper
Received: 2015/04/18 | Accepted: 2016/10/29 | Published: 2017/07/18 | ePublished: 2017/07/18

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