Deep Learning-based Approach using Recurrent Neural Network for Classification of the Human Activities

Abstract

Information can be obtained through classification and recognition systems of human activities at any time. These systems are used in different areas such as disease detection, improvement of physical therapy stages, development of smart home projects, and etc. In this study, data taken from a public data set obtained from accelerometer and gyroscope sensors in smart phones were used. Most of the studies in the literature cannot analyze higher level attributes and their relationships based on time series processing with artificial neural network model. The Long-Short Term Memory (LSTM) model is a very suitable deep learning approach due to its ability to obtain relationships for time series as a recurrent neural network and to be flexible in its layers. The deep learning-based approach that includes this infrastructure has been used in the classification of various human activities in our experiments. In the experiments, different input parameters, layer and network units were given to related network models and classification performance accuracy rate was measured. As a result, a classification performance of approximately 86% to 93% was obtained, showing that six different classes were classified with high accuracy. Discussion and scientific findings are also included in the study.

Keywords

• Human Activities,Deep Learning

İnsan Aktivitelerinin Sınıflandırılmasında Tekrarlayan Sinir Ağı Kullanan Derin Öğrenme Tabanlı Yaklaşım

Abstract

Bireyler üzerinden istenildiği anda insan aktivitelerini sınıflandırma ve tanıma sistemleri ile bilgi elde edilebilmektedir. Bu sistemler hastalıkların tespiti, fizik tedavi aşamalarının iyileştirilmesi, akıllı ev projelerinin geliştirilmesi gibi farklı alanlarda kullanılmaktadır. Bu çalışmada akıllı telefonlardaki ivmeölçer ve jiroskop duyargalarından elde edilmiş halkın kullanımına açık bir veri kümesinden alınan veriler kullanılmıştır. Literatürdeki çalışmaların çoğu yapay sinir ağı modeliyle zaman serilerinin işlenmesine dayanan daha yüksek seviyeli öznitelikleri ve bunların aralarındaki ilişkileri çözümleyememektedir. Uzun-Kısa Süreli Bellek (UKSB) modeli tekrarlayan sinir ağı olarak hem zaman serileri için ilişki elde edebilmesi hem de katmanlar halinde kullanılabilen esnek yapısı nedeniyle oldukça uygun bir derin öğrenme yaklaşımıdır. Bu altyapıyı içeren derin öğrenme tabanlı yaklaşım çalışmamızdaki deneylerde çeşitli insan aktivitelerinin sınıflandırılmasında kullanılmıştır. Deneylerde farklı girdi parametreleri, katman ve ağ birimleri ilgili ağ modellerine verilerek sınıflandırma başarımı doğruluk oranı ölçülmüştür. Sonuçta yaklaşık %86 ilâ %93 arasında sınıflandırma başarımı elde edilerek altı farklı sınıfın yüksek doğrulukta sınıflandırıldığı gösterilmiştir. Çalışmada buna dair tartışma ve elde edilen bilimsel bulgulara da yer verilmektedir.

Keywords

• İnsan Aktiviteleri,Derin Öğrenme,Uzun-Kısa Süreli Bellek

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