## 4 Experiments and Results
### Experimental Results of Position-Dependent HAR
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Position**} & \multicolumn{3}{c}{**Recognition Effectiveness**} \\ \cline{2-4}  & **Accuracy (Mean \(\pm\) std.)** & **Loss (Mean \(\pm\) std.)** & **F1-Score (Mean \(\pm\) std.)** \\ \hline Chest & 98.08\% (\(\pm\)0.49\%) & 0.09 (\(\pm\)0.02) & 98.12\% (\(\pm\)0.37\%) \\ Forearm & 94.74\% (\(\pm\)0.31\%) & 0.24 (\(\pm\)0.02) & 94.75\% (\(\pm\)0.33\%) \\ Head & 96.62\% (\(\pm\)0.21\%) & 0.17 (\(\pm\)0.01) & 96.70\% (\(\pm\)0.20\%) \\ Shin & 97.89\% (\(\pm\)0.17\%) & 0.10 (\(\pm\)0.01) & 97.90\% (\(\pm\)0.07\%) \\ Thigh & 98.59\% (\(\pm\)0.26\%) & 0.07 (\(\pm\)0.01) & 98.52\% (\(\pm\)0.31\%) \\ Upper arm & 96.96\% (\(\pm\)0.44\%) & 0.14 (\(\pm\)0.01) & 96.93\% (\(\pm\)0.46\%) \\ Waist & 98.01\% (\(\pm\)0.29\%) & 0.09 (\(\pm\)0.01) & 97.93\% (\(\pm\)0.33\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracies and F1-scores of the proposed Att-ResBiGRU model for position-dependent HAR across all seven positions on the REALWORLD16 dataset. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Position**} & \multicolumn{3}{c}{**Recognition Effectiveness**} \\ \cline{2-4}  & **Accuracy (Mean \(\pm\) std.)** & **Loss (Mean \(\pm\) std.)** & **F1-Score (Mean \(\pm\) std.)** \\ \hline Back & 89.06\% (\(\pm\)1.15\%) & 0.52 (\(\pm\)0.07) & 85.69\% (\(\pm\)1.76\%) \\ RUA & 88.72\% (\(\pm\)1.25\%) & 0.53 (\(\pm\)0.10) & 84.90\% (\(\pm\)1.45\%) \\ RLA & 88.14\% (\(\pm\)1.12\%) & 0.53 (\(\pm\)0.08) & 83.99\% (\(\pm\)1.58\%) \\ LUA & 86.79\% (\(\pm\)0.78\%) & 0.60 (\(\pm\)0.05) & 82.56\% (\(\pm\)1.00\%) \\ LLA & 84.76\% (\(\pm\)0.72\%) & 0.66 (\(\pm\)0.02) & 79.84\% (\(\pm\)1.20\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracies and F1-scores of the proposed Att-ResBiGRU model for position-dependent HAR across all five positions on the Opportunity dataset. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Position**} & \multicolumn{3}{c}{**Recognition Effectiveness**} \\ \cline{2-4}  & **Accuracy (Mean \(\pm\) std.)** & **Loss (Mean \(\pm\) std.)** & **F1-Score (Mean \(\pm\) std.)** \\ \hline Hand & 97.12\% (\(\pm\)0.18\%) & 0.12 (\(\pm\)0.01) & 96.95\% (\(\pm\)0.16\%) \\ Chest & 97.59\% (\(\pm\)0.23\%) & 0.10 (\(\pm\)0.01) & 97.46\% (\(\pm\)0.25\%) \\ Ankle & 97.25\% (\(\pm\)0.11\%) & 0.11 (\(\pm\)0.01) & 97.17\% (\(\pm\)0.13\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracies and F1-scores of the proposed Att-ResBiGRU model for position-dependent HAR across all three positions on the PAMAP2 dataset. * Conversely, the accuracy and F1-score for the forearm position were the lowest, at 94.74% and 94.75%, respectively, indicating that forearm motion was the least indicative of the activities. * Upper-body positions, including the chest, upper arm, and abdomen, consistently performed well, with accuracies above 97%. This finding supports the notion that the movement of the upper body provides valuable information regarding numerous everyday activities. * Notably, the standard deviations for the accuracies and F1-scores were less than 0.5% across all positions, suggesting minimal variance between trials and indicating the consistent effectiveness of the model. * Generally, the low loss values indicate that the model effectively aligned with the training data across different positions. To sum up, the model exhibited robust position-dependent HAR on the REALWORLD16 dataset, with the most distinct signals arising from the motion of the thigh. The low variance in performance emphasizes the model's reliable generalization.