# Sleep Posture Recognition Method Based on Sparse Body Pressure Features
## 5 Results
### Comparison of Results  
We empirically assessed the performance of GP, XGBoost, RF, SVM, CNN, and our suggested enhanced model in classification tasks. The efficacy of each model was rigorously evaluated using four principal metrics: Accuracy, Precision, F1 score, and Kappa statistic.  
Table 4 indicates that the performance of the GP was comparatively suboptimal, achieving an accuracy of 91.54%. This limitation arises from the model's assumption of feature independence, which may hinder its adaptability to complicated real-world data. XGBoost and RF demonstrated superior performance, achieving Accuracies of 93.18% and 94.21%, respectively, confirming the efficacy of the ensemble learning model in handling high-dimensional and nonlinear data. The SVM achieved an Accuracy of 94.31%, with Precision and F1 index values of 94.35% and 93.31%, respectively, indicating its robust generalization capability on high-dimensional feature data. CNN enhanced its classification performance, achieving an Accuracy of 94.66%, the highest among all base-learner models.  
Our enhanced model demonstrated substantial advancements across all evaluation parameters, achieving Accuracy, Precision, F1, and Kappa of 95.63%, 95.70%, 95.63%, and 94.90%, respectively, thereby surpassing other models thoroughly. This results from the comprehensive integration of various complementary base learning models to enhance the model's accuracy and resilience, with the deep learning amalgamation of auto-encoder and CNN for the extraction of more profound features. This outcome illustrates that our suggested model can proficiently encapsulate the intricate characteristics of the data while also markedly enhancing the accuracy and stability of the classification task through its unique model architecture and algorithm optimization.  
To validate the superiority of our proposed model in integration learning, we conduct a thorough comparison with existing classical integration learning approaches, including TreeBag, AdaBoost, Voting, and Stacking. The results in Figure 13 demonstrate that our model surpassed the other techniques across multiple evaluation metrics. This signifies that our approach possesses significant advantages in sleep posture identification and can offer a more dependable solution.  
Table 5 depicts a comparison between our current studies and the most sophisticated and representative experiments available. The utilization of RGB and thermal cameras is significantly more precise than non-contact sensors, as they may directly capture body contour maps, facilitating improved access to body position markers, albeit potentially disclosing private information. Our method demonstrates significantly greater precision compared to the approach utilizing triple ultra-wideband radar. In comparison to the contact sensors, our approach demonstrates a more superior performance relative to the method employing accelerometer sensors and conventional pressure sensors, attributable to the latter's collection of greater interference data. Nonetheless, our findings are inferior to those of the intensive airbag pressure sensor, which uses more sensors and more accurate  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **F1** & **Kappa** \\ \hline GP & 91.54\% & 91.61\% & 91.53\% & 90.62\% \\ XGBoost & 93.18\% & 93.20\% & 93.18\% & 92.33\% \\ RF & 94.21\% & 94.25\% & 94.15\% & 93.57\% \\ SVM & 94.31\% & 94.35\% & 94.31\% & 93.65\% \\ CNNs & 94.66\% & 94.77\% & 94.64\% & 94.06\% \\ Our model & 95.63\% & 95.70\% & 95.63\% & 94.90\% \\ \hline \hline \end{tabularsensors. In contrast, our sparse airbag sensor technology is more appropriate for everyday usage due to its lower cost and ease of maintenance.