## 5 Result and Analysis
### Recognition of Common Human Movements  
Across the various human activity categories, the most prevalent ones include walking, standing, climbing upstairs, descending stairs, and lying down. In most scenarios, recognition often revolves around these primary actions. Therefore, we proceeded to extract these six key movement categories from both the MultiportGAM dataset and the PAMAP2 dataset. This focused evaluation allows us to assess our hybrid human pose recognition method's classification performance specifically on these commonly encountered activities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline GAM-MLP & 93.96 & 93.89 & 93.91 \\ SVM & 82.84 & 82.43 & 82.58 \\ CNN-M [36] & 93.74 & 93.28 & 93.85 \\ LSTM-CNN & 92.63 & 92.61 & 92.89 \\ FE-CNN [37] & 91.66 & 91.43 & 91.40 \\ DCL & 92.49 & 92.42 & 92.30 \\ CE-HAR [38] & 92.14 & 92.43 & 92.18 \\ IN & 91.77 & 91.76 & 91.47 \\ TL-HAR [39] & 92.33 & 91.83 & 92.08 \\ ConvAE-LSTM [40] & 94.33 & - & 94.46 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different methods for the PAMAP2 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline GAM-MLP & 96.13 & 96.12 & 96.13 \\ SVM & 82.70 & 82.70 & 82.69 \\ DCL [33] & 92.11 & 92.10 & 92.11 \\ IN [34] & 92.72 & 92.72 & 92.71 \\ LSTM-CNN [35] & 94.23 & 94.10 & 94.17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of different methods for the MultiportGAM dataset.  
The experimental outcomes, illustrated in Figures 12 and 13, highlight how the hybrid human pose recognition method performs when tasked with classifying these frequently observed actions within the MultiportGAM dataset and the PAMAP2 dataset.  
It is evident that due to variations in the number and placement of sensors, the performance of GAM-MLP varies across different datasets. Comparing the two datasets, unlike the PAMAP2 dataset, the MultiportGAM dataset collects accelerometer, magnetometer, and gyroscope data from the chest and limbs, using two additional IMUs. In terms of daily behavior, the MultiportGAM has a longer duration, resulting in higher recognition accuracy.  
On the MultiportGAM dataset, our GAM-MLP model achieves an average accuracy of 97.45% across the six common daily activities. This represents a 1.04-10.8% enhancement over other models. Notably, the highest accuracy is achieved in recognizing the \"sitting down\" action, reaching an accuracy of 98.94%. Even in the challenging \"descending stairs\" action, the model reaches an impressive precision of 93.67%. Similarly, on the PAMAP2 dataset, the \"sitting down\" action boasts the highest recognition rate of 99.02%, while the \"descending stairs\" action presents the lowest recognition rate at 93.74%. The average accuracy improves by 1.59-11.06% across the board. Through the combination of the multi-layer perceptron and group attention module, we achieve the information exchange and fusion between different features of the same group. In the training process, the neural network automatically learns and optimizes the weight and bias through the back-propagation  
Figure 12: Classification performance of six commonly used actions on the MultiportGAM dataset.  
Figure 13: Classification performance of six commonly used actions on the PAMAP2 dataset.  
algorithm, so that the neurons can better capture the correlation information between different features, in order to improve the accuracy and reliability of the classification.