Abstract
Traditional methods for pattern recognition have demonstrated significant advancements in recent years. However, these techniques have traditionally relied on human intervention to discern crucial insights from data. Deep learning has ushered in a transformative shift by empowering computers to autonomously glean knowledge from data, particularly benefiting our understanding of how individuals interact with mobile and wearable technology. The burgeoning popularity of deep learning stems from its ability to operate effectively with minimal or no human guidance. This research introduces an innovative hybrid deep learning architecture that seamlessly integrates Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) layers. While maintaining precision in activity identification, this CNN-MLP approach excels in localized feature extraction, made possible through the synergy of CNN and MLP layers. Our model delivered outstanding performance on the UCI HAR dataset, achieving a classification accuracy of 97.14%.