## 3 Proposed Method  
Figure 1 outlines our specialized model architecture, leveraging MobileNetV2 for feature extraction in a dynamic human-action-recognition system coupled with the Deep BiLSTM framework. MobileNetV2's strength in handling dense connections enhances model performance. Our methodology integrates a novel Deep BiLSTM classification model, refining hierarchical features from MobileNetV2 for enhanced discriminative power and nuanced understanding of temporal dependencies in dynamic human actions. This model was meticulously designed to excel in extracting robust action features, particularly adept at efficiently handling diverse and detailed images, addressing the computational challenges associated with managing numerous pixel values. A distinctive strength of MobileNetV2 lies in its adept handling of dense connections, where each pixel value communicates with every other neuron. This intrinsic capability significantly contributes to preserving the model's performance, ensuring optimal outcomes in action recognition tasks. The efficiency of our model is further heightened through the integration of powerful techniques. Leveraging convolution and a 3 \(\times\) 3 MaxPooling filter and employing strategic batch normalization and dropout layers, we optimized the features. The results from these layers, presented in multidimensional matrices, seamlessly undergo conversion to a one-dimensional format using a flattened layer. This transformation facilitates effective utilization by the subsequent dense layer. Choosing ReLU and softmax activation strategically enhances the computational efficiency and prediction accuracy. Acknowledging the resource-intensive nature of training CNN models, we strategically capitalized on the efficiency of pre-existing models, with MobileNetV2 playing a pivotal role in our approach. This deliberate choice not only enhances the computational efficiency, but also harnesses the proven capabilities of MobileNetV2 in our specific study context. Our methodology used a novel Deep BiLSTM classification model for refining hierarchical features extracted by MobileNetV2. This hierarchical refinement enhances discriminative power by understanding sequential dependencies in action sequences. Deep BiLSTM excels in capturing long-term temporal dependencies, providing a nuanced perspective on dynamic human actions. The model dynamically adapts to varying temporal contexts, ensuring robust performance across different action scenarios. Configurable layer and neuron explorations showcase adaptability, and iterative fine-tuning enhances responsiveness to real-world variations. Algorithm 1 elucidates our proposed methodology. In the algorithm, we included every step we performed in our study. In step 1, we used a preprocessing technique to convert the video into a sequence of frames and make a data frame. In step 2, we used a pre-trained model, namely MobileNetV2, to extract features from the data frame. The main reason behind MobileNetV2 is that, to stabilize the computational complexity, we used the multi-stage deep learning technique. In step 3, we employed the Deep BiLSTM model to refine the feature and the classification. After that, we trained the model with a strong tuning technique, where it trained multiple times, which is equivalent to the number of epochs. We combined each batch-oriented prediction for training and testing cases and used the combined prediction value to calculate the accuracy score. In step 4, we trained the model and made validation results. In step 5, we showed the display result in terms of accuracy. In step 6, we included real-time testing, where the user can give any video-based activity recognition, and our model would produce the activity name in English.