## 4 Results and Discussion
### Four-Sensor Configurations  
#### 4.4.1 Four-Sensors: Model Performance with Non-Overlapping Data  
In this configuration, four sensors--accelerometer, gyroscope, linear accelerometer, and gravity--were utilized with non-overlapping data segments. The inclusion of additional sensors aims to capture a wider range of movement patterns, potentially enhancing classification accuracy across different models. This strategy leverages the unique strengths of each sensor, allowing for a more comprehensive analysis of monitored activities. The four-sensor setup enriches the feature set and provides nuanced information about the subject's movements, aiding in distinguishing between similar activities. For instance, the gyroscope contributes to rotational movement detection, while the linear accelerometer and gravity sensor provide insights into linear acceleration and gravitational effects. This multidimensional approach is expected to improve the models' ability to recognize subtle variations in activity patterns. The evaluation results in Table 18 reveal that the **TCN** model achieves the highest performance across most metrics, with an F1 Score of **0.9770** on the test set. This model demonstrates strong consistency across training, validation, and test sets, underscoring its robustness in handling non-overlapping configurations for human activity recognition. DT and KNN models offer reliable baseline performance, with DT reaching an F1 Score of 0.9719 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 0.9753, while XGBoost achieves a higher F1 Score of 0.9735, 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 0.9735, making it a strong candidate for activity recognition in configur \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 & 0.9723 & 0.9709 & 0.9719 & 0.9723 & 0.9709 & 0.9719 & 0.9723 & 0.9709 & 0.9719 & 0.9723 & 0.9709 & 0.9719 \\ \hline KNN & 0.9681 & 0.9681 & 0.9681 & 0.9681 & 0.9681 & 0.9681 & 0.9681 & 0.9681 & 0.9681 & 0.9681 & 0.9681 \\ \hline RF & 0.9755 & 0.9750 & 0.9753 & 0.9755 & 0.9750 & 0.9753 & 0.9755 & 0.9750 & 0.9753 & 0.9755 & 0.9750 & 0.9753 \\ \hline SVC & 0.9739 & 0.9754 & 0.9735 & 0.9739 & 0.9754 & 0.9735 & 0.9739 & 0.9754 & 0.9735 & 0.9739 & 0.9754 & 0.9735 \\ \hline XGBoost & 0.9774 & 0.9801 & 0.9735 & 0.9774 & 0.9801 & 0.9735 & 0.9774 & 0.9801 & 0.9735 & 0.9774 & 0.9801 & 0.9735 \\ \hline GRU & 0.9735 & 0.9767 & 0.9738 & 0.9735 & 0.9767 & 0.9738 & 0.9735 & 0.9767 & 0.9738 & 0.9735 & 0.9767 & 0.9738 \\ \hline LSTM & **0.9756** & **0.9793** & 0.9752 & **0.9756** & **0.9793** & **0.9752** & **0.9756** & **0.9793** & **0.9752** & **0.9756** & **0.9793** & **0.9752** \\ \hline
**TCN** & **0.9775** & **0.9759** & **0.9770** & **0.9775** & **0.9759** & **0.9770** & **0.9775** & **0.9770** & **0.9775** & **0.9759** & **0.9770** \\ \hline Transformer & 0.9763 & 0.9755 & 0.9760 & 0.9763 & 0.9755 & 0.9760 & 0.9763 & 0.9755 & 0.9760 & 0.9763 & 0.9755 & 0.9760 \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Performance comparison of four sensors with Non-overlapping data for various models, where the performance metrics are Accuracy, Precision, Recall, and F1 Score. ficient handling of non-overlapping data.