## 5 Result and Analysis
### Performance on Different Datasets  
We proceed to compare our hybrid human activity recognition method with both baseline techniques and several cutting-edge techniques on the MultiportGAM and PAMAP2 dataset. We evaluate each model's performance using three key metrics: accuracy, recall, and F1-score. Accuracy can intuitively measure the model's ability to accurately predict human activity categories. The recall rate represents the proportion of the number of behavior categories correctly predicted by the model to the actual number of human behaviors. A high recall rate means that the model can more comprehensively detect human behavior and reduce missed recognition. The F1-score provides a more comprehensive evaluation of the model's performance in human behavior recognition tasks. A model with a high F1-score demonstrates strong performance in terms of both precision and recall, being able to more accurately recognize human behavior and possessing good overall performance. This comprehensive analysis aims to uncover the effectiveness of our hybrid human pose recognition approach in the realm of human activity recognition.  
The results of our comparisons on the MultiportGAM dataset are presented in Table 3, offering a clear perspective on the respective performances. Similarly, the comparison outcomes with the PAMAP2 dataset are summarized in Table 4. By examining the met  
Figure 11: Accuracy and Loss of GAM-MLP on both training and test sets.  
rics across these datasets, we gain valuable insights into how our hybrid human pose recognition method performs in comparison to other established and advanced techniques.  
Based on the comparative results, it is evident that leveraging the human pose angle model constructed from raw sensor data and the GAM-MLP model's attention mechanism, we achieved remarkable performance on the MultiportGAM Dataset. Our hybrid approach attained an accuracy of 96.13%, a recall rate of 96.12%, and an F1-score of 96.13%. In contrast to the baseline methods, our approach demonstrated notable improvements of 13.43%, 13.42%, and 13.44% in the respective accuracy, recall, and F1-score metrics.  
The hybrid approach surpasses the currently established cutting-edge methods, showcasing improvements in recognition accuracy ranging from 1.65% to 5.54%. These findings underscore the efficacy of our hybrid approach in advancing the field of human activity recognition, substantiating its superiority over both conventional baselines and advanced methodologies.  
The comparative results of the PAMAP2 dataset show that the hybrid approach demonstrates certain advantages, with its recognition performance surpassing that of other state-of-the-art methods except for ConvAE-LSTM. Even when compared to ConvAE-LSTM, the hybrid approach lags behind in recognition accuracy by only 0.39%. However, the hybrid approach boasts lower model parameters and complexity, Our method reduces the overall parameter count by 19-21%, making it more advantageous for deployment on mobile devices. In summary, our human body pose model excels at extracting posture angles of various body parts to the fullest extent. By efficiently utilizing sensor data information, GAM-MLP effectively distinguishes and relates the differences and connections between actions when focusing on intra-group features. This process leads to precise recognition outcomes.