## 1 Introduction  
Human activity recognition (HAR) is one of the most essential topics of computer vision concerning the last two decades and has been used in various areas such as video-based surveillance systems [1], elderly care [2], education [3], and healthcare [4; 5; 6; 7]. HAR provides information about human physical activity and aims to discover simple or complex procedures in a very realistic environment. To recognize human activities at the highest accuracy, HAR presents the right diagnosis of activity models obtained from various sensors. Sensors used in HAR applications consist of three clusters that are cameras, wearable sensors, and gyro sensors [8; 9; 10; 11; 12]. General approaches address a HAR problem in two main categories as vision-based and non-vision based systems. Vision-based HAR systems combine different methods with advanced applications using image processing. However, non-vision based HARsystems extract the relevant features coming from the sensor and recognize the activity using a proper machine learning classifier. Both methods have positive and negative sides compared to each other. For instance, non-vision based systems work better in terms of environmental conditions such as fixed scenes and lack of lighting and occlusion. On the other hand, vision-based sensors are much cost effective and are more useful in daily life applications (video surveillance systems) [13]. For this study, we decided on using the vision-based sensor since vision-based sensors fit more for daily life use and are more affordable. There have been various studies for vision-based human activity recognition in the literature. Despite the fact that there exist human activity recognition systems based on RGB cameras, researches indicate that dark environment and illumination changes are still challenging problems. In order to overcome this issue, illumination-invariant systems have been developed using depth sensors. In this regard, a human posture recognition system based on a single depth camera was presented in [14] where skeleton information is articulated with rotated Gaussian kernels and indexed in tree structure. Another study conducting the same issue is also presented in [15] where 3D transformations of each skeletal joint are indexed in twist and exponential maps to construct a deformation model to be employed for the recognition and pose tracking of the objects within the range of a single depth camera. In addition, spatiotemporal behavior of human activities is extracted by depth sensors using the cosine distances among 3D skeleton joints [16]. Similarly, in [17], 3D pose estimation system is proposed in which multiple body joints are constructed in a per-pixel classification problem by combining confidence scored intermediate body parts. Different from RGB cameras and depth sensors, various sensors have been employed to achieve human activity recognition such as multiple accelerometers, pyroelectric sensors, and wearable sensors. In [18], authors use wearable sensors to perform human behavior analysis. In [19], motion sensors are employed to analyze daily motion activities. Another study employed pyroelectric sensors to recognize abnormal human activities [20]. In [21], smart phones are used for human activity recognition based on the analysis of signals that come from motion sensors. Additionally, internet of things (IoT) technology is used for human activity recognition by employing different machine learning methods [7]. On the other hand, various feature types, data structures and machine learning approaches were employed to obtain better performance in human activity recognition. In [6], a healthcare application based on a single camera is proposed in which multiple features are classified by means of a Hidden Markov Model (HMM). In other study, spatial-temporal features are extracted and analyzed in [22; 23]. In [24], graph structure is employed for abnormal human activity recognition.