## 2 Related Work  
This section provides a brief review of the research work related to this study, primarily in relation to three key issues: the representation of features extracted from human skeletal joints, the modeling of temporal dynamics across successive frames in a video sequence for action recognition and approaches for the efficient extraction of key frames from video sequences. Deep learning has automated the process of representation learning in many application areas, especially computer vision [21]. In [22], the authors proposed using a deep convolutional neural network called the high-resolution network (HRNet) for pose tracking on the PoseTrack dataset. HRNet automatically extracts the human skeleton feature vectors for action recognition. Meanwhile, in [23], the authors proposed a multi-level representation of Fisher vectors and other skeleton-based geometric features for the recognition of hand gestures. In [24], the authors presented a novel representation method called _PoTion_, which uses heat maps for human joints to encode the movement of some semantic keypoints. In [25], the authors asserted that optical flow-based techniques are more effective for action recognition, as they are invariant to appearance, are more accurate at boundaries and yield better action recognition performance even with small displacements. Moreover, the detection and extraction of key frames in videos has been found to be an effective strategy in many application areas, such as action recognition and video classification. Different techniques have been proposed for the extraction of key frames in videos. For instance, [PERSON] et al. [9] proposed a deep learning-based approach for the detection of key frames in videos. They proposed using a two-stream deep ConvNet for the detection of key frames in a video. Their model is trained to directly predict the locations of key frames in a video. Similarly, in [8], the authors proposed a self-supervised and automatic method to select key frames in a video. In their approach, these authors proposed to combine a two-stream ConvNet with a novel annotation architecture that automatically annotates key frames in a video. Moreover, [PERSON] et al. [26] proposed another method for filtering key frames in a video, which works by aggregating the video frames instead of looking at them one by one. Similarly, different approaches have been proposed for the modeling of temporal dynamics across successive frames for action recognition. For instance, in [27], [PERSON] et al. proposed a systematic approach for the recognition of human activities in real time using OpenPose and a long short-term memory (LSTM) recurrent neural network (RNN). The authors in [28] proposed using a hierarchical RNN for modeling the temporal dynamics associated with human actions across successive frames, whereas in [29] a temporal sliding LSTM network was proposed for the same purpose. Similarly, [PERSON] et al. [30] proposed a fast and lightweight network (i.e., the double-feature double-motion network (DDNet)) for skeleton feature-based action recognition. Moreover, [PERSON] et al. [7] proposed using a key frame-based approach for 3D action recognition using a deep neural network. In their study, they performed extensive experiments on the motion capture database _HDM05_[31], which contains more than 4 h of recorded motion video, and the Carnegie Mellon motion capture database, which is divided into 6 categories and 23 subcategories [32]. In [33], the authors demonstrated that dense sampling methods are more effective for human action recognition compared with methods that rely on sparse interest point representations, which may overlook some important space structures and hence lead to higher misclassification. Many authors have tried to compensate for the loss of important structural information in methods based on local representations. For instance, in [34], the authors explored the use of spatiotemporal structural information and neighborhood-based features.