## 4 Results and Discussion
### Two-Sensor Configurations  
#### 4.3.1 Two-Sensors: Model Performance with Non-Overlapping Data  
In this configuration, data from two sensors--an accelerometer and a gyroscope--were analyzed using non-overlapping segments. The models evaluated include DT, KNN, RF, SVC, XGBoost, GRU, LSTM, TCN, and Transformer. The evaluation results in Table 16 reveal that the **TCN** model achieves the highest performance across most metrics, with an F1 Score of **0.9757** on the test set. This model demonstrates strong consistency across training, validation, and test sets, underscoring its robustness and suitability for non-overlapping configurations in human activity recognition. DT and KNN models offer reliable baseline performance, with DT reaching an F1 Score of 0.9725 on the test set. However, these simpler models tend to lag behind more advanced models, particularly when handling high-dimensional sensor data [33; 34]. Among the ensemble models, RF and XGBoost exhibit solid performance, leveraging ensemble learning to enhance generalization. RF achieves a test F1 Score of 0.9720, while XGBoost achieves a higher F1 Score of 0.9753, reflecting the benefits of boosting techniques for managing complex data interactions [26; 27]. SVC also demonstrates adaptability to non-linear data with a test F1 Score of 0.9726, making it a strong candidate for activity recognition in configurations lacking temporal overlap [36]. The deep learning models GRU and LSTM offer consistent results, with F1 Scores of 0.9748 and 0.9738, respectively, on the test set, highlighting their effectiveness in sequential data processing even without overlapping segmentation [37; 38]. Despite its strengths, the Transformer model performs slightly lower than TCN in this configuration, with an F1 Score of 0.9724 on the test set, suggesting that while its self-attention mechanism is beneficial for long-range dependencies, TCN may be more effective in handling structured, sequential data in non-overlapping setups. In conclusion, the **TCN** model emerges as the best performer in this configuration, demonstrating high accuracy, precision, recall, and F1 Score across all sets. Its superior \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1 Score**} \\ \cline{2-13}  & **Train** & **Validation** & **Test** & **Train** & **Validation** & **Test** & **Train** & **Validation** & **Test** & **Train** & **Validation** & **Test** \\ \hline DT & 0.9726 & 0.9724 & 0.9725 & 0.9726 & 0.9724 & 0.9725 & 0.9726 & 0.9724 & 0.9725 & 0.9726 & 0.9724 & 0.9725 \\ \hline KNN & 0.9693 & 0.9723 & 0.9703 & 0.9693 & 0.9723 & 0.9703 & 0.9693 & 0.9723 & 0.9703 & 0.9693 & 0.9723 & 0.9703 \\ \hline RF & 0.9759 & 0.9792 & 0.9720 & 0.9759 & 0.9792 & 0.9720 & 0.9759 & 0.9792 & 0.9720 & 0.9759 & 0.9792 & 0.9720 \\ \hline SVC & 0.9742 & 0.9752 & 0.9726 & 0.9742 & 0.9752 & 0.9726 & 0.9742 & 0.9752 & 0.9726 & 0.9742 & 0.9752 & 0.9726 \\ \hline XGBoost & 0.9755 & 0.9767 & 0.9753 & 0.9755 & 0.9767 & 0.9753 & 0.9755 & 0.9767 & 0.9753 & 0.9755 & 0.9767 & 0.9753 \\ \hline GRU & 0.9760 & 0.9765 & 0.9748 & 0.9760 & 0.9765 & 0.9748 & 0.9760 & 0.9765 & 0.9748 & 0.9760 & 0.9765 & 0.9748 \\ \hline LSTM & 0.9761 & 0.9724 & 0.9738 & 0.9761 & 0.9724 & 0.9738 & 0.9761 & 0.9724 & 0.9738 & 0.9761 & 0.9724 & 0.9738 \\ \hline
**TCN** & **0.9762** & **0.9768** & **0.9757** & **0.9762** & **0.9768** & **0.9757** & **0.9762** & **0.9768** & **0.9757** & **0.9762** & **0.9768** & **0.9757** \\ \hline Transformer & 0.9724 & 0.9745 & 0.9724 & 0.9724 & 0.9745 & 0.9724 & 0.9724 & 0.9745 & 0.9724 & 0.9724 & 0.9745 & 0.9724 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Performance comparison of two sensors with non-overlapping data for various models, where the performance metrics are Accuracy, Precision, Recall, and F1 Score. generalization capabilities highlight its robustness for human activity recognition in non-overlapping data settings.