Deep learning techniques effectively extract discriminative parameters from body-worn sensor data for human activity recognition (HAR). We propose an attention-based HAR model to enhance precision and interpretability. Our approach demonstrates superior recognition performance on benchmark datasets and provides attention maps, improving decision-making understanding. This innovation advances HAR and opens new research opportunities in AI-powered activity recognition, benefiting both academia and industry. The innovation presented in this paper not only contributes to the evolution of HAR methodologies but also marks a significant step forward in advancing the broader landscape of AI-powered activity recognition. By fostering a deeper understanding of intricate activity patterns through attention mechanisms, our approach not only benefits academia in refining state-of-the-art models but also holds promise for practical applications in industry. The insights gained from this research pave the way for new avenues of exploration and development in the realm of AI-driven human activity recognition.