Abstract

In the domain of deep learning, Human Activity Recognition (HAR) models stand out, surpassing conventional methods. These cutting-edge models excel in autonomously extracting vital data features and managing complex sensor data. However, the evolving nature of HAR demands costly and frequent retraining due to subjects, sensors, and sampling rate variations. To address this challenge, we introduce Cross-Domain Activities Analysis (CDAA) combined with a clustering-based Gated Recurrent Unit (GRU) model. CDAA reimagines motion clusters, merging origin and destination movements while quantifying domain disparities. Expanding our horizons, we incorporate image datasets, leveraging Convolutional Neural Networks (CNNs). The innovative aspects of the proposed hybrid GRU_CNN model, showcasing its superiority in addressing specific challenges in human activity recognition, such as subject and sensor variations. This approach consistently achieves 98.5% accuracy across image, UCI-HAR, and PAMAP2 datasets. It excels in distinguishing activities with similar postures. Our research not only pushes boundaries but also reshapes the landscape of HAR, opening doors to innovative applications in healthcare, fitness tracking, and beyond.