Open AccessComparison of Artificial Neural Networks and Genetic Programming Methods For Activity Recognition
Author(s) -
Çağatay Berke Erdaş,
Tunç Aşuroğlu,
Koray Açıcı,
Hasan Oğul
Publication year - 2018
Publication title -
akıllı sistemler ve uygulamaları dergisi
Language(s) - English
Resource type - Journals
ISSN - 2667-6893
DOI - 10.54856/jiswa.201805019
Subject(s) - genetic programming , artificial neural network , computer science , artificial intelligence , perceptron , activity recognition , wearable computer , genetic algorithm , context (archaeology) , pattern recognition (psychology) , machine learning , accelerometer , multilayer perceptron , wavelet , raw data , embedded system , paleontology , biology , operating system , programming language
With the widespread use of wearable sensors, the processing of raw data obtained from sensors has led to widely-used solutions to the problem of activity recognition. In this context, it is aimed to compare the performance of artificial neural network methods (ANN, RBFNN) and genetic programming (GP) methods over time, frequency and wavelet features extracted from the accelerometer data. The most successful classification performance achieved was 75.09% using 31 neurons in the hidden layer of the multilayer perceptron, using time attributes.