## 5 Discussion
### Comparison Results with State-of-the-Art Models  
To underscore the importance and dependability of the proposed Att-ResBiGRU in both position-dependent and position-independent HAR, this research conducted a comparison of performance using the same benchmark HAR datasets employed in this study, namely the PAMAP2 and REALWORLD16 datasets, against state-of-the-art studies. When scrutinizing the outcomes of position-dependent HAR on the PAMAP2 dataset, as presented in Table 6, several noteworthy observations can be made regarding the performance of our Att-ResBiGRU model compared to earlier state-of-the-art approaches. Most notably, our model achieved superior F1-scores across all three positions of wearable  
Figure 6: Comparison results of five baseline deep learning models and the proposed Att-ResBiGRU model in position-independent HAR. devices--wrist, chest, and ankle. Specifically, we obtained a 96.95% F1-score for the hand, 97.46% F1-score for the chest, and 97.17% F1-score for the ankle. This signifies a noteworthy improvement of over 3% in absolute terms compared to the best-performing random forest (RF), support vector machine (SVM), and decision tree (DT) [22] models for the hand position. Similarly, enhancements of nearly 1% were observed compared to previous works [22] utilizing chest and ankle sensors. Examining the outcomes of HAR based on sensor positions in the REALWORLD16 dataset, as displayed in Table 7, revealed consistent enhancements in performance through the application of our proposed Att-ResBiGRU methodology when compared to prior state-of-the-art approaches [54, 55] across all seven sensor locations. Notably, we attained F1-scores exceeding 96% on the chest, upper-arm, and waist positions, surpassing 94% on the wrist/forearm, and exceeding 97% on the head and shin positions using the proposed Att-ResBiGRU model. This signifies substantial improvements in F1-scores ranging from 3% to 13% in absolute terms when compared to the previously top-performing two-stream CNN [54] and random forest (RF) model [55]. In position-independent HAR, our examination involved assessing the effectiveness of Att-ResBiGRU by comparing it with currently established leading techniques outlined in [54, 55, 56]. We opted for this comparison because the authors provided evaluations on the identical PAMAP2 and REALWORLD16 datasets. The comparative analysis is presented in Table 8, showcasing the superior results achieved by Att-ResBiGRU compared to previous studies' findings. Examining the outcomes presented in Table 8, which compares the performance of our proposed Att-ResBiGRU model with existing cutting-edge techniques in position-independent human activity recognition on the PAMAP2 and REALWORLD16 benchmark datasets, revealed highly promising advancements in performance. On the PAMAP2 dataset, our methodology achieved an F1-score of 96.44%, surpassing all baseline methods such as RF, SVM, k-nearest neighbors (KNN), Bayes Net (NB), and DT, which exhibited F1-scores ranging from 78% to 91%. Similarly, on the REALWORLD16 dataset, we attained a 96.23% F1-score, outperforming previous works utilizing CNN, LSTM, and multi-layer perceptron (MLP) architectures that reported F1-scores between \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{3}{c}{**F1-Score(\%)**} \\ \cline{2-4}  & **Hand** & **Chest** & **Ankle** \\ \hline RF [22] & 93.50\% & 96.20\% & 94.40\% \\ SVM [22] & 90.60\% & 94.10\% & 92.50\% \\ K-NN [22] & 77.30\% & 80.40\% & 75.70\% \\ BN [22] & 78.50\% & 83.70\% & 78.80\% \\ DT [22] & 82.60\% & 87.70\% & 86.60\% \\ \hline Att-ResBiGRU & 96.95\% & 97.46\% & 97.17\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison results with state of the art in position-dependent HAR using sensor data from the PAMAP2 dataset. \begin{table}