## 3 Related Works
### Traditional Features-Based Classifiers with Skeleton Dataset for Human Action Recognition  
The commonly used classifiers including SVM, k-nearest neighbors, hidden markov model (HMM), dynamic time warping (DTW), extreme learning machine (ELM), and Bayesian classifier are considered for skeleton-based human activity recognition. The above-mentioned discrimination methods require meaningful hand-crafted features for the classification of human behaviors. Video-based human action recognition depends on the spatial information of each frame and temporal changes of all frames. To perform hand-crafted features-based human action discrimination, the spatial information of each frame and temporal information between neighboring frames must have to be captured and combined.  
[PERSON] et al. [46] introduced new features called the histogram-based representation of 3D human posture from the projected views of depth sequences using linear discriminant analysis. The temporal information was kept by using HMM. In [47], [PERSON] et al. presented a novel approach where they extracted features based on EigenJoints of joint positions differences and conducted Naive Bayes Nearest Neighbor classifier for action recognition. [PERSON] et al. [48] demonstrated a new method for human action recognition by fusing spatio-temporal motion information and frame difference with the pairwise distance of skeleton joint coordinates. The spatio-temporal information determined by 3D interest point detection and local feature descriptor using Harris3D detector. The geometric relationship between various human body parts was exposed by [PERSON] et al. [43] using rotations and transformation in 3D space. The performance was evaluated with DTW, Fourier temporal pyramid, and linear SVM by the representation of the lie group. [PERSON] et al. [49] illustrated a local descriptor from skeleton joints that maintained view-invariant features. They conducted Fisher kernel to explain the skeleton quads in anaction and performed the classification using traditional SVM. A fast and powerful method for human action recognition using the 3D skeleton dataset was published by [PERSON] et al. [50] in which they considered a moving pose descriptor containing pose information, speed, and acceleration of joints. [PERSON] et al. [51] assumed the skeleton sequences of action as a set of spatio-temporal poses. First, they normalized the joint coordinate and computed temporal variations. Then SVM was used to differentiate among the bag of poses (BOP) and finally histogram features were mined for the purposes of action classification using ELM. An effective multi-instance learning idea was brought out by [PERSON] et al. [52] to find the discriminative multi-instance multi-task learning (MIMTL) features to expose the relationship among the skeleton joints. They also conducted multi-task learning model to recognize human action with MIMTL.  
The previous hand-crafted features-based methods require an active engagement along with a lot of efforts to extract the feature of spatial and temporal information from the skeleton sequences. Sometimes, it becomes more complicated to design discriminative features from the 3D skeleton videos that degrades the performance of the system.