## 6 Conclusions and Future Works  
This paper introduces ConvResBiGRU-SE, an inventive deep-learning architecture for HAR employing wearable sensor data. The proposed model effectively integrates CNN, residual connections, BiGRU, and SE mechanisms to attain state-of-the-art implementation on standard HAR benchmarks. Our experimental results highlight several significant contributions to the field: (1) ConvResBiGRU-SE model achieves superior recognition accuracy compared to existing methods, reaching 99.18% accuracy on the UCI-HAR dataset and 98.78% on the WISDM dataset. These results represent substantial advancements over previous state-of-the-art approaches, demonstrating the efficacy of our architectural design choices; (2). Our comprehensive ablation studies validate the significance of each component in the architecture. The convolutional blocks enhance feature extraction capabilities, while the SE-Residual blocks enhance feature recalibration and model stability. Integrating bidirectional GRU units captures temporal dependencies in sensor data, leading to more robust activity recognition; and (3). We address the critical challenge of model interpretability in HAR systems by integrating Grad-CAM visualization techniques. These visualizations deliver insights into the decision-making procedure of the model, revealing how different sensor channels and temporal regions contribute to activity classification. This advancement towards explainable HAR is essential for creating confidence in real-world applications. Future research should optimize the model for real-time use on resource-constrained devices through compression and quantization. Transfer learning can enhance cross-dataset generalization, and personalization strategies can tailor the model for individual users while ensuring privacy and efficiency. Additionally, extending the framework to recognize complex, long-duration activities, incorporating more sensor modalities, and developing advanced visualization methods will improve interpretability. Energy efficiency is also crucial, balancing accuracy and power consumption for wearable devices. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Reference** & **Year of Publication** & **Model** & **Accuracy (\%)** \\ \hline [PERSON] et al. [48] & 2018 & U-Net & 97.00\% \\ [PERSON] et al. [49] & 2018 & KNN & 96.20\% \\ [PERSON] et al. [50] & 2020 & RNN-LSTM & 94.00\% \\ [PERSON] et al [47] & 2022 & Ens-HAR & 98.70\% \\ The proposed model & - & ConvResBiGRU-SE & 98.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance comparison of state-of-the-art model on the WISDM dataset. **Author Contributions:** Conceptualization, S.M. and A.J. ; methodology, S.M. ; software, A.J. ; validation, A.J. ; formal analysis, S.M. ; investigation, S.M. ; resources, A.J. ; data curation, A.J. ; writing--original draft preparation, S.M. ; writing--review and editing, A.J. ; visualization, S.M. ; supervision, A.J. ; project administration, A.J. ; funding acquisition, S.M. and A.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This research budget was allocated by the University of Phayao; the Thailand Science Research and Innovation Fund (Fundamental Fund 2025, Grant No. 5014/2567); National Science, Research and Innovation Fund (NSRF); and King Mongkut's University of Technology North Bangkok (Project no. KMUTNB-FF-68-B-02). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** To clarify, our research utilizes a pre-existing, publicly available dataset. The dataset has been anonymized and does not contain any personally identifiable information. We have cited the source of the dataset in our manuscript and have complied with the terms of use set forth by the dataset provider.