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A study on human activity recognition with analysis of angles between skeletal joints

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  1. Tez No: 622884
  2. Yazar: ÖMER FARUK İNCE
  3. Danışmanlar: PROF. DR. DANIŞMAN YOK
  4. Tez Türü: Doktora
  5. Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2018
  8. Dil: İngilizce
  9. Üniversite: Kyungsung Unıversıty
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: Belirtilmemiş.

Özet

Human activity recognition (HAR) has become effective in computer vision for video surveillance systems and medical purposes such as monitoring elder people in home environment. There are various types of sensors which can be used to address this task. However, RGB-depth sensors, mostly the ones used for gaming purposes, are cost effective and enable to provide data about environment. In this thesis, a biometric system that can detect human activities in 3D space is proposed. Previous approaches have shown that monitoring of various physiological signals can provide distinctive information to recognize human activities. The proposed method conducts a machine learning to determine a pattern on numerous activities using angles between joints in 3D space. Simply, activity related skeletal joint angles are obtained using RGB-depth sensor. Since HAR is operated in a time domain, swinging angle information is stored by sliding kernel method which creates a feature set. A Haar-wavelet transform (HWT) then is applied not to lose the important information in the signal before reducing dimension. After that, dimension reduction using averaging algorithm is also applied to decrease number of features and computational cost. Before the classification, proposed inverse HWT is applied. By this means, new feature set is extracted. Lastly, k-nearest neighbor (k-nn) algorithm is used to recognize the activity with respect to the given data. Since k-nn is lazy learning algorithm, computational cost is quite low compare to other machine learning algorithms. The performance of the proposed method for various activities show that human activity recognition rate is 86.1%. To see the effectiveness of the proposed method, there are two types of comparisons; firstly, k-nn performances based on different 'k' value are shown. Secondly, test performance is observed based on different classifiers. It is expected that proposed algorithm can make a contribution for improvement of the HAR

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