Improved Pattern Recognition Techniques for Monitoring Human Activity Recognition in Digital Platforms through Image Processing Techniques

With the rapid advancement of digital platforms and the increasing prevalence of visual data, the need for accurate and efficient human activity recognition has become paramount. In this paper, a novel approach that leverages image processing and hybrid deep learning techniques to enhance pattern recognition for monitoring human activity in digital platforms is proposed. The proposed framework begins with a sensor-captured image to the preprocessing step where raw image data is cleaned, normalized, and transformed into suitable feature representations. Various image processing techniques such as image enhancement, noise reduction, and feature extraction are employed to enhance the discriminative power of the input data. A hybrid deep learning architecture is developed that combines the benefits of various deep learning models to accomplish robust and accurate human activity recognition. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms are specifically combined to extract both spatial and temporal information from the input images. The CNNs are in charge of picking up spatial features, while the RNNs and attention mechanisms, respectively, record temporal relationships and emphasize significant areas in the images. To train and evaluate the proposed framework, large-scale datasets containing diverse human activities are utilized. Experimental results demonstrate the superior performance of the proposed approach compared to state-of-the-art methods in terms of accuracy, precision, and recall. The hybrid deep learning architecture effectively handles complex human activity recognition tasks, accurately identifying and categorizing various activities in digital platforms.