## 2 Related Work  
One of the major research fields of computer vision is action recognition, which is also a part of gesture recognition [30; 31]. Many researchers have employed numerous methods to design action-recognition systems, including AI, hand-crafted features with conventional machine learning algorithms, and various deep learning methodologies [32; 33; 34; 35; 36]. The existing HAR is mainly developed with various handcrafted-feature-extraction techniques and traditional machine learning algorithms. Traditional approaches to action recognition in machine learning typically follow a three-phase methodology. The first phase involves extracting features using handcrafted descriptors, followed by encoding these features using a specific algorithm. The final phase entails classifying the encoded features using a suitable machine learning algorithm [37; 38]. Within computer vision, two primary types of feature-extraction techniques are prevalent: local-feature-based and global-feature-based approaches. Local-feature-based methods describe features as independent patches, interest points, and gesture information that align with learned cues for a given task. In contrast, global features are represented by the area of interest, often described through background subtraction and tracking techniques [39; 40]. In the realm of conventional machine learning, researchers have devised effective systems for action recognition by leveraging handcrafted features. Notable examples include VLAD [41] and BOW [42], which contribute to the rich landscape of methodologies aiming to enhance the recognition accuracy of actions within diverse datasets. This established paradigm showcases the versatility of machine learning in processing and interpreting visual data, particularly in the context of recognizing actions or movements. Handcrafted feature extractors are often tailored for specific datasets, making them domain-specific and unsuitable for general-purpose feature learning [43]. The specificity of these extractors restricts their applicability beyond the datasets for which they were designed. Recognizing the need for efficiency, some researchers have employed keyframe-based strategies to reduce the processing time in their systems [44]. For instance, [PERSON] et al. [45] introduced a key technique for action recognition in video sequences involving keyframe selection and its application to HAR. Similarly, [PERSON] et al. [46] proposed multi-feature fusion-based HAR using keyframes through conventional machine learning methods. While conventional machine learning algorithms have seen significant success in the past decade, they are constrained by human cognition and encounter challenges such as time consumption, labor intensiveness, and the complexity of feature engineering selection [47]. Recognizing these limitations in handcrafted-based HAR, researchers are increasingly turning to deep learning to devise efficient and innovative techniques for advanced video-based HAR systems. This shift toward deep learning reflects a quest for more-automated and -adaptive approaches that can overcome the limitations posed by traditional handcrafted features. Diverging from the conventional three-step machine learning architecture, deep learning brings forth a contemporary end-to-end structure, enabling the simultaneous learning and representation of high-level discriminative visual features during classification [48]. Notably, prevalent end-to-end (CNN) architectures dynamically adjust the parameters based on the data, utilizing convolutional operations to learn optimal features [49]. While CNN-based approaches excel in learning features from 2D data, they may not be directly applicable to 3D data representations. To address this, some researchers have introduced methods that employ 3D filters instead of 2D when learning features from video frames [44; 50].