By Rezaul Begg, Marimuthu Palaniswami
Fresh years have noticeable many new advancements in computational intelligence (CI) thoughts and, accordingly, this has ended in an exponential raise within the variety of functions in various parts, together with: engineering, finance, social and biomedical. specifically, CI options are more and more getting used in biomedical and human stream components as a result of the complexity of the organic structures in addition to the constraints of the prevailing quantitative suggestions in modelling.Computational Intelligence for stream Sciences: Neural Networks and different rising ideas comprises information about state of the art study results and state-of-the-art expertise from prime scientists and researchers engaged on a variety of features of the human move. Readers of this booklet will achieve an perception into this box in addition to entry to pertinent details, which they are going to be in a position to use for carrying on with examine during this quarter.
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Extra resources for Computational Intelligence for Movement Sciences: Neural Networks, Support Vector Machines, and Other Emerging Technologies
There are two required outcomes to this process: 1. 2. to reduce the amount of data to a manageable level (dimension reduction), and to keep the most important features of the data and eliminate all the redundant features of the data (feature selection). The idea is to provide a “summary” which can be used to give a meaningful interpretation of the data. The objective of the first step towards feature selection is Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc.
EMG does not provide the quantitative accuracy needed for the assessment of muscle force generation, although the linear envelope is thought to resemble the muscle force curve (Bartlett, 1994). All of the above relates to time-domain analysis of EMG signals. Another way of analysing the signal is via a frequency domain analysis. Converting raw EMG-time data to the frequency domain is a process called harmonic or spectral analysis, and can be done by Fourier analysis. The end result of this is a power spectrum.
While this process is a form of feature selection, it is by itself a pseudorandom feature selection process because there is no means of knowing which parameters are relevant and which are not. If using the Benedetti et al. feature-set, some method is required to determine which parameters are relevant and which are not. One way to achieve the goal of reducing the number of parameters and keeping only the relevant parameters is to use principal components analysis (discussed later). Reproducible Features As mentioned earlier, some time-series “features” that occur in normal gait do not occur in all/some strides of every individual and in other population groups.
Computational Intelligence for Movement Sciences: Neural Networks, Support Vector Machines, and Other Emerging Technologies by Rezaul Begg, Marimuthu Palaniswami