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

Yıl 2019, Cilt: 2 Sayı: 2, 1 - 10, 30.12.2019

İbrahim Ali Metin , Bahadır Karasulu

Öz

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.

Anahtar Kelimeler

Human Activities, Deep Learning

İnsan Aktivitelerinin Sınıflandırılmasında Tekrarlayan Sinir Ağı Kullanan Derin Öğrenme Tabanlı Yaklaşım

Öz

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.

Anahtar Kelimeler

İnsan Aktiviteleri, Derin Öğrenme, Uzun-Kısa Süreli Bellek

Kaynakça

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