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AKILLI SÄ°STEMLER VE UYGULAMALARI DERGÄ°SÄ°
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Comparison of Artificial Neural Networks and Genetic Programming Methods For Activity Recognition

Aktivite Tanımada Yapay Sinir Ağları ve Genetik Programlama Yöntemlerinin Karşılaştırılması

How to cite: ErdaÅŸ ÃB, AÅŸuroÄŸlu T, Açıcı K, OÄŸul H. Comparison of artificial neural networks and genetic programming methods for activity recognition. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2018; 1(1): 62-66.

Full Text: PDF, in Turkish.

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Title: Comparison of Artificial Neural Networks and Genetic Programming Methods For Activity Recognition

Abstract: 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.

Keywords: Artificial neural networks; genetic programming; activity recognition; sensor

Başlık: Aktivite Tanımada Yapay Sinir Ağları ve Genetik Programlama Yöntemlerinin Karşılaştırılması

Özet: Giyilebilir sensörlerin yaygınlaşmasıyla beraber sensörlerden elde edilen ham verilerin işlenmesiyle aktivite tanıma problemine getirilen çözümler yaygınlaşmaya başlamıştır. Bu bağlamda literatürde çeşitli uygulamalar olmakla beraber, bu çalışmada aktivite tanımada popüler olarak kullanılan yapay sinir ağı yöntemleri (ANN, RBFNN) ve genetik programlama (GP) yönteminin ivmeölçer verisinden çıkarılan zaman, frekans ve dalgacık (wavelet) öznitelikleri üzerinden performans karşılaştırılması hedef alınmıştır. Bahsi geçen çalışma kapsamında alınan sonuçlara bakıldığında, ulaşılan en başarılı sınıflandırma performansı zaman öznitelikleri kullanılarak, çok katmanlı perseptronun ara katmanında 31 nöron kullanılması ile %75.09 olarak elde edilmiştir.

Anahtar kelimeler: Yapay sinir ağları; genetik programlama; aktivite tanıma; sensör

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