Ensembled PreTrained Convolutional Neural Network Techniques for Human Activity Detection and Recognition

Human Activity Recognition (HAR) plays a pivotal role across a broad spectrum of applications, ranging from healthcare monitoring to enhancing security systems and refining human-computer interaction. This research introduces an advanced ensemble approach that integrates multiple Convolutional Neural Networks (CNNs), each engineered to extract unique features and representations from benchmark datasets encompassing a variety of human activities. The synergy of these CNNs within our ensemble framework has led to a marked improvement in recognizing a diverse array of activities, underscoring the efficacy of CNNs in elevating HAR's capabilities. Moreover, we propose a methodological framework that harnesses the collective strength of ensemble CNNs, aiming to boost the accuracy and robustness of activity recognition. This innovative approach not only sets a new standard in achieving high precision in HAR but also opens new avenues for deploying more dependable and precise human activity recognition systems in real-life scenarios.