Human Activity Recognition (HAR) has emerged as a critical research domain with applications spanning healthcare monitoring to smart environments. Despite significant advances in deep learning approaches for HAR, fundamental challenges persist in modeling the inherent variability of human movements and the sequential nature of physical activities. In this paper, we present a Multi-Head Convolutional Neural Network with an attention mechanism (MH-CNN) that addresses these limitations by dynamically focusing on relevant sensor inputs. Our approach integrates four parallel convolutional pathways with attention mechanisms, enabling the model to process different feature subspaces while maintaining computational efficiency simultaneously. Experiments conducted on the challenging Opportunity dataset (Roggen in UCI Mach Learn Repos, 2010. https://doi.org/10.24432/C5M027 ) [6] reveal that our architecture achieves superior performance (99.78% test accuracy and F1-score), outperforming conventional CNN approaches (99.75%) and single-head attention models (99.73%). While the accuracy improvement of 0.03% may appear modest, it represents approximately 30 additional correctly classified instances in our test set of approximately 10,000 samples, which is meaningful in safety–critical HAR applications where misclassifications can have significant consequences. Interestingly, we identify and analyze an unexpected phenomenon where single- head attention slightly underperforms compared to standard CNNs, while multi- head configurations provide substantial improvements. These findings demonstrate that our architecture effectively captures the complex temporal dependencies in human activities, offering a promising direction for next- generation HAR systems in real-world applications.