## 1 Introduction  
Human action recognition has been grabbing more attention among researchers due to its multitude of real-world applications, for example, human-machine or human-object interaction [1, 2, 3], smart and intelligent surveillance system [4, 5, 6], content-based data retrieval [7], virtual reality/augmented reality [8, 9], health care system [10, 11], autonomous driving [12], and games [13]. The demand for human action recognition as well as pose estimation [14] is also expanding significantly, as a result, to manage the time, avoid intimate contact during the pandemic of diseases, and provide comfortable interaction for the rehabilitees. The human action recognition system focuses on identifying the activity accurately about what type of behavior is undertaken in a sequence of frames of so-called video. With the progress of modern technology, various sensor devices such as Microsoft Kinect, Z-Depth, Leap Motion Controller, and Intel RealSense [15] have been invented to capture human activity data. Different modalities, for instance, RGB, depth, and skeleton data [16] are mainly used by the researchers for video-based human action recognition. Even though notable success has been appeared in the human action recognition system in the last few years, it is still challenging to accurately predict human activity for various restrictions such as camera orientation, occlusions, background, the variation of length, and speed of action [17]. Several methods offered a variety of ideas about human action recognition using hand-crafted features as well as using deep-learning features. Hand-crafted features are usually referred to as the extraction of meaningful information (such as edges and corners) present in the images or videos using various descriptors e.g., local binary pattern (LBP) [18], space-time interest points (STIP) [19], scale-invariant feature transform (SIFT) [20], speeded up robust features (SURF) [21], histograms of oriented gradient (HOG) [22], and histograms of optic flow (HOF) [23]. These descriptors generate discriminative features by extracting information from locally important patches to represent action sequences. [PERSON] et al. [19] proposed a method for action recognition using trajectory-based feature representation where meaningful spatial information was preserved by detecting STIPs with SIFT and temporal change between the consecutive frames in an RGB video was computed by matching the interest points. Another hand-crafted feature-based method was introduced by [PERSON] et al. [24] with spatio-temporal interest point to detect the interest points in the video. They took the help of HOG and HOF descriptors to extract appearance and motion features to perform classification using support vector machine (SVM). In [25], [PERSON] et al. combined local and global features extracted from RGB sequences and performed classification with SVM. They designed the shape descriptor as a local descriptor by integrating 3D trajectory and motion boundary histogram and extracted global features with gist descriptor for classification. The engagement of modern researchers in the field of deep learning helps to design several learning models such as convolutional neural network (CNN) [26], recurrent neural network (RNN) [27], and long short-term memory (LSTM) [27]. These well-known models are widely being used to extract deep features for human action recognition. A two-stream CNN model was designed by [PERSON] et al. [28] for human action recognition in RGB videos. The proposed CNN models consisting of spatial and temporal networks covered both local and global changes in the sequences for action discrimination. [PERSON] et al. [29] considered the time complexity to calculate the optical flow and suggested a deeply transferred motion vector CNN model to take the scope of optical flow. In [30], an effective deep 3D CNN model was introduced by [PERSON] et al. to dig up the spatio-temporal features and recognized the action classes. [PERSON] et al.