## 5 Conclusions and Future Works  
This research aimed to investigate the recognition of transitional activities using DL and to develop the HAR model using a hybrid deep residual network. The proposed HAR model, named the SEResNet-BiGRU model, initially learns hybrid spatial features from raw sensor data via a convolutional block, and then learns spatio-temporal features via SEResidual blocks composed of an SE module in the main path and a BiGRU in the parallel path of the connection.  
Physical activities identified in this article comprised everyday fundamental activities and transitional activities gathered from two benchmark datasets (HAPT and MobiAct v2.0). According to the experimental findings, the suggested hybrid model outperformed other models in the HAR challenge (98.03% for the HAPT dataset and 98.92% for the MobiAct v2.0 dataset). Additionally, we evaluated the suggested model in comparison to previously published models for the same transitional HAR problem. The comparison findings demonstrate that the SEResNet-BiGRU model outperforms all prior models.  
Nevertheless, in the situation of the MobiAct scenario dataset, one constraint of the proposed model is visible. Because the smartphone is held on an arbitrary side of the pants and in an arbitrary orientation, there are certain instances in which the model cannot distinguish between walking, walking upstairs, and walking downstairs. In both datasets, transition classes are sometimes misclassified concerning their associated static postures. There is no precise border between the prior posture, the transition, and the subsequent posture, which creates the issue of many activities occurring inside the same data window.  
In future studies, we will emphasize the generalization of our proposed model by incorporating additional data segmentation techniques to address the constraints: for instance, event-defined window strategies which require preprocessing to identify specific events that are then used to specify successive data partitioning, and activity-defined window strategies, which partition data based on activity adjustments.  
Figure 10: The change of model accuracy and loss in the training and validation data of the SEResNet-BiGRU on MobiAct v2.0 dataset.  
Figure 9: The change of model accuracy and loss in the training and validation data of the SEResNet-BiGRU on HAPT dataset.  
**Author Contributions:** Conceptualization and model analysis, S.M.; resource, N.H.; data curation, A.J.; methodology, S.M.; validation, N.H.; data visualization and graphic improvement, A.J.; discussion and final editing, S.M.; writing--review and editing, S.M.; funding acquisition, A.J. and S.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research project was supported by University of Phayao (Grant No. FF65-RIM041), Thailand Science Research and Innovation Fund, National Science, Research and Innovation Fund (NSRF), and King Mongkut's University of Technology North Bangkok with Contract no. KMUTNB-FF-65-27.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.