TY - JOUR ID - 593 TI - Application of statistical techniques and artificial neural network to estimate force from sEMG signals JO - Journal of AI and Data Mining JA - JADM LA - en SN - 2322-5211 AU - Khoshdel, V. AU - Akbarzadeh, A. R AD - Center of Excellence on Soft Computing & Intelligent Information Processing, Mechanical Engineering Department, Ferdowsi University of Mashhad, Mashhad. Y1 - 2016 PY - 2016 VL - 4 IS - 2 SP - 135 EP - 141 KW - Artificial Neural Network KW - Taguchi method KW - Analysis of variance KW - EMG signals DO - 10.5829/idosi.JAIDM.2016.04.02.02 N2 - This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There are plenty of algorithms that are used to obtain the optimal ANN setting. However, to the best of our knowledge they did not use regression analysis to model the effect of each parameter as well as present the percent contribution and significance level of the ANN parameters for force estimation. In this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi help us to find the optimal parameters settings. Next, analysis of variance (ANOVA) technique is used to obtain significance level as well as contribution percentage of each parameter to optimize ANN’s modeling in human force estimation. The results indicated that design of experiments is a promising solution to estimate the human force from sEMG signals. UR - https://jad.shahroodut.ac.ir/article_593.html L1 - https://jad.shahroodut.ac.ir/article_593_6db5e7ffbd72ce4aae887b4881c00a09.pdf ER -