## 1 Introduction  
Human activity recognition (HAR) plays a crucial role in diverse real-life domains, including intelligent video surveillance, abnormal activity detection, action-based video retrieval, semantic video recognition, and healthcare patient monitoring [1, 2]. Action recognition, especially from online data streams derived from visual sensors in surveillance, website videos, and social media feeds, holds the potential to detect anomalies, identify fraudulent activities, and discern abnormal situations [1]. In video analysis, human actions are discerned through the movement of distinct body parts, such as hands and legs. Unlike static images, a single frame cannot fully convey the essence of an action [3]. For example, the initial action pose of jumping for a headshot in football and skipping rope may appear similar. The distinction between these actions becomes apparent when observed across a sequence of frames, capturing the nuanced movements and interactions of the human body with its surroundings [4, 5]. In non-stationary data streams, the effectiveness of a trained model over previous data diminishes when faced with variations in new data. Adaptability to the new data distribution is crucial for addressing this challenge, requiring diversity in a non-stationary environment [6]. [PERSON] et al. framed this as an optimization problem, employing a bio-inspired algorithm to address drift heterogeneity and achieve diversity through a self-learning optimization technique [7]. [PERSON] et al. introduced a novel approach, modifying the weighted one-class SVM to enhance its analysis of non-stationary streaming data [8]. They emphasized the adaptability of a one-class classifier's decision boundary to new data streams, aided by a forgetting mechanism, which facilitates the re-learning of parameters. [PERSON] et al. proposed an efficient ensemble learning technique that iteratively adjusts Naive Bayes classifier weights, enabling smooth adaptability to the current stream situation without an external drift detector [9]. [PERSON] et al. conducted a comprehensive survey on activity recognition in online data stream mining [10]. However, accurately recognizing human actions in real-time from online surveillance data streams remains challenging due to factors such as high-dimensional features, variations in viewpoint, motion, cluttered backgrounds, occlusion, and diverse illumination conditions [11; 12; 13]. To address these challenges, the domain of HAR has witnessed the extensive use of handcrafted local feature descriptors over the past decade [14; 15]. The drawbacks of handcrafted-feature-extraction mechanisms lie in their rigid engineering, the representation of low-level semantics in visual data, and the high complexity associated with both extraction and classification. Consequently, researchers have shifted towards the exploration of automatic-feature-learning methods [15]. Automatic-feature-learning methods, particularly those based on neural networks, offer a more-adaptive approach. These methods can directly extract features from raw inputs based on the learned weights and biases of the network. Convolutional Neural Networks (CNNs) exemplify this approach, employing a hierarchical learning process. The initial layers capture local features from visual data, while the final layers extract global features representing high-level semantic features. In recent developments, researchers have been exploring various techniques, including CNN-based methods, transfer learning, and Transformers, to enhance performance accuracy in sequence learning for action recognition. Notably, BT-LSTM, as employed by [16], achieved an accuracy of 85.3% on the UCF11 dataset. Similarly, KFDI [17], Dilated CNN+BiLSTM+RB [18], local-global features+QSVM [19], the dynamic human activity recognition work [20], the 3 DCNN [21], and deep autoencoder+CNN [22] methods yielded accuracy rates of 79.40%, 89.00%, 82.60%, 85.20%, 96.20%, and 92.40%, respectively, for the UCF11 dataset.