Hybrid Deep Learning Architecture for Efficient Human Activity Recognition: A CNN-Attention-BiLSTM Framework

Human Activity Recognition (HAR) has emerged as a critical research area in the domains of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) due to its extensive applications across various domains. The development of robust HAR models capable of accurately identifying human activities is growing in demand. This study aims to advance the field by introducing a novel hybrid model that integrates Convolutional Neural Networks (CNN), Attention mechanisms, and Bidirectional Long Short-Term Memory (BiLSTM) networks. This “CNN-Attention-BiLSTM” model is meticulously designed to capture both spatial and temporal features, thereby enhancing feature extraction and attentiveness. We have evaluated the proposed model using the widely recognized UCI-HAR dataset. The results demonstrate that our model achieves an impressive activity classification accuracy of 93%. To ensure the reliability and validity of our findings, we employed rigorous validation techniques, including cross-validation and detailed classification reports. The model successfully met these validation criteria, confirming its effectiveness and innovation.