## 1 Introduction
[31] proposed a long-term recurrent convolutional network with doubly deep compared to a fixed simple spatio-temporal receptive field for sequential processing. By using a long-term recurrent network, they captured complex dynamic to classify action groups. Dataset captured in RGB format suffers from view dependency, background and illumination sensitivity, and computational complexity. While acquired an image or video of action in RGB format, it generates a lot of pixel values that makes it more complicated to differentiate from the background. The afore-mentioned obstacles hinder the performance of RGB video-based human action recognition and persuade to adopt the capturing devices to generate the depth and other formats of images or videos. [PERSON] et al. [32] proposed an efficient method for action recognition by extracting LBP features from depth motion map representation of three different views in depth sequences. They performed the classification by combining two fusion methods (feature-level fusion and decision-level). The integration of local and global features generated from depth sequences was introduced by [PERSON] et al. [33]. They took the advantages of the correlation among the poses in neighboring frames of action to get the modified bag-of-words model called the bag of correlated poses. The dimensionality of the feature map was reduced with the help of principal components analysis and classified using SVM. [PERSON] et al. [34] presented a CNN features-based method for human action recognition using depth sequences. The features were extracted by training a CNN model with multi-channel inputs such as two consecutive frames and projected view on the Cartesian plane. Finally, a LSTM was trained to determine the classification results. In [PERSON] et al. (2020), [PERSON] et al. encoded the depth maps into weighted hierarchical depth motion maps (WHDMM) in three orthogonal planes. Then a three-branch CNN was conducted to pick out the discriminative features from WHDMM for the classification of human action. Even though the depth images or videos require very-little storage compared to RGB, it also sustains from color, texture, and shape information. Meanwhile, both RGB and depth videos are captured using traditional cameras in 2D space. Thus, human action recognition based on RGB and depth sequences lack more deep information due to its 2D orientation and cannot capture the 3D structure of the action. By considering the following limitation, many sensor devices such as Microsoft Kinect provides more optimized information about the human body in terms of twenty skeleton joints. The skeleton information is represented in a deep 3D structure that is view-independent. Thus, the acquisition of an image or video in 3D skeleton format is much faster, lightweight in storage, and easy to use in the fields of human action recognition. Human action recognition based on skeleton data requires the exploitation of spatial and temporal changes of 3D skeleton joints in the sequences of action. There are several methods that suggest skeleton-based human action discrimination ranging from hand-crafted features based on human action classification using traditional machine learning algorithms to deep features based on human action recognition using deep learning. To provide more discriminative local and temporal features to improve recognition performance, skeleton joints information is represented in spatial format by capturing motion. Thus, it is very important to map the 3D skeleton joints in such a way that can cumulate both spatial and temporal information. In this paper, we propose a new 3D skeleton-based human action recognition method by mapping the skeleton joints into spatio-temporal image by joining line between the same joints in two adjacent frames. Initially, we draw the position of joints by putting pixels with different colors in the jet color map (a color generated from the jet color map is the array of red, green, and blue intensities ranging from 0 to 1) in each frame.