Abstract
Wearable devices have promoted the application of Human Activity Recognition to the development of techniques for the assessment or diagnosing of illnesses and seizures, among other applications. For instance, the use of tri-axial accelerometry (3DACM) to detect abnormal and sudden movements has been introduced in the epileptic seizure recognition. In a previous research, Fuzzy Rule Based Classifiers (FRBC) have been found valid for the detection of epileptic convulsions; however, Ant Colony Systems learned FRBC performed with a high variability depending on the training data. In this study, we cope with this problem by the selection of a suitable partitioning method that has been extended to generate Fuzzy partitions. The comparison with the previous obtained results shows the fuzzy partitioning does not improve the overall performance in terms of error but highly reduces the variability in the performance of the obtained models, which allows us to obtain general models.
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