İvmeölçer ve Nesnelerin İnterneti Tabanlı Gerçek Zamanlı İnsan Aktivite Tespiti

İnsan aktivite tespiti son zamanlarda popülerliği artan bir makine öğrenmesi problemidir. Hareketi tespit etmek için ivmeölçer, jiroskop v.b sensörler veya kamera yardımıyla görüntü işleme yapılarak tahminler yapılabilmektedir. Bireylerden sensörler vasıtasıyla alınan veriler ön işlemden geçerek sınıflandırma algoritmaları ile sınıflandırılarak kişilerin hangi hareketi yaptıkları tespit edilmeye çalışılmaktadır. Bu çalışma kapsamında mobil cihaz için yapılan android yazılım ile cihazın ivmeölçer sensörü kullanılarak nesnelerin interneti tabanlı insan hareketlerinin tespiti gerçekleştirilmiştir. İlk önce tespiti yapılacak hareketler için veri toplanmıştır ve ön işlemden geçirilmiştir. Daha sonra oluşan veri setinden özellik çıkarımı yapılmıştır. Elde edilen veri üzerine Destek Vektör Makinaları, Rastgele Orman ve K En Yakın Komşuluk algoritmaları uygulanarak yapılan hareketler sınıflandırılmıştır. Sınıflandırma başarıları tespit edilmiş olup en başarılı sınıflandırma algoritması nesnelerin interneti tabanlı uygulama ile gerçek zamanlı sınıflandırma işlemi için kullanılmıştır.

Real-Time Human Activity Detection Based on Accelerometers and Internet of Things

Human activity detection is a machine learning problem that has recently become more popular. Estimations can be done using image processing by means of accelerometer, gyroscope, sensor or camera to detect movement. The data obtained from the individuals through sensors is pre-processed and classified with classification algorithms to determine which movement they make. Within the scope of this study, the internet based human movements were determined by using the accelerometer sensor of a device with an android software made for the mobile device. First, data was collected and pre-processed for the movements to be determined. Then feature extraction was performed from the data set. The movements were classified by using Support Vector Machines, Random Forest and K Nearest Neighbour algorithms on the resulting data. Classification successes have been determined and the most successful classification algorithm has been used for real time classification of objects with internet based application.

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