Human Activity Recognition (HAR) has applications in healthcare, assistive technology, and security, where both interpretability and accuracy are necessary for real-world implementation. Existing deep learning methods such as CNN-LSTM hybrids have a tendency to behave as uninterpretable "black boxes," lowering the confidence of users in real deployments. In this work, we present a novel HAR framework with explainability built directly into the architecture and training process. Our approach integrates Squeeze-and-Excitation (SE) attention into a CNN-LSTM backbone for recalibrating feature importance, and introduces a hierarchical interpretability strategy that uncovers both sensor-level and temporal phase-level relevance for activity recognition. To render explanations reliable, we design a consistency-based regularization objective that fosters stable and sparse attention patterns across samples, making interpretability intrinsic to the learning process rather than an afterthought. Furthermore, we present a phase-aware visualization method that maps attention weights to sensor modalities and activity phases, offering intuitive and actionable insights to domain experts. Experimental evaluation on a real-life HAR dataset demonstrates that the proposed framework achieves above 96% classification accuracy, outperforming conventional multiheaded CNN-LSTM, while offering robust and interpretable explanations of activity patterns. This work takes HAR to the next level by integrating high predictive power with intrinsic trust and interpretability, paving the way for deployment in safety-critical domains.