*5.3.1 Support Vector Machines*

In order to identify speech pattern using a wearable textile-based sensor [10], the best results were obtained with Support Vector Machines (SVMs). The basic approach for SVM algorithms is to give a set of basic examples and their weight, generally understood as positive and negative (binary) examples for the algorithm, interpreted as classes, where there is a degree of similarity between them, a kernel function, as a means of comparison [52].

SVM was applied for identifying activities using an acoustic sensor [50]. They used more than two classes, comparing one against the other as a strategy to obtain results, using the Radial Basis Function (RBF) as a kernel function. All the implementation using a library "LIBSVM" [54] reaches almost 80% of accuracy.
