# Sleep Posture Recognition Method Based on Sparse Body Pressure Features
## 6 Discussion  
The results show that the sleeping posture recognition model constructed based on the barometric pressure sensor shows good performance, and it can accurately recognize multiple sleeping posture categories. The system achieves a high level of accuracy both in the seven well-designed and clearly defined sleeping postures, and in the four vaguely defined experimental conditions of supine, side lying, prone, and edge without limitation of limb postures to validate the generality of the system. This sleep posture recognition system has extensive practical applications. In the healthcare sector, it can assist specialists in monitoring patients' sleeping positions. For patients with certain ailments, suitable sleeping positions can mitigate symptoms and enhance recovery. Additionally, real-time monitoring and feedback on patients' sleeping postures enable healthcare providers to offer more precise sleep coaching. This system can autonomously modify the mattress's firmness based on the user's sleeping position, enhancing comfort while simultaneously gathering real-time data on the sleeping posture to mitigate risks of falls and sleep disorders. Nonetheless, it is important to acknowledge the existing limitations of this study. This sleep position identification system was tested solely during a brief trial period and within a controlled environment, leaving its efficacy in more intricate and prolonged scenarios of full-night sleep unexamined and unassessed. Subsequent research must prioritize the long  
Figure 13: Experimental comparison with other integrated learning models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Type** & **Model** & **Accuracy** \\ \hline RGB and thermal cameras [16] & non-contact & \
\begin{tabular}{c} Normally distributed \\ sequences \\ \end{tabular} & 99\% \\ Triple ultra-wideband & non-contact &
\begin{tabular}{c} Swin Transformer \\ \end{tabular} & 80.8\% \\ Triaxial accelerometer sensors & & & \\
[23] & contact & KNN & 93\% \\ Single accelerometer [24] & contact & AnpoNet & 94.67\% \\ Airbag pressure sensor [29] & contact & CNNs & 99.3\% \\ Airbag pressure sensor [30] & contact & AdaBoost-SVM & 99.9\% \\ Pressure sensors [31] & contact & SVM & 90.00\% \\ Sparse airbag pressure sensor & contact & Our model & 95.63\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with different sensor methods of recognizing sleeping position. night sleep scenario to enhance the system's recognition accuracy and reliability during prolonged nocturnal rest. This can be achieved by optimizing the algorithms, enhancing the sensor performance, or employing advanced data processing techniques, thereby ensuring the sleep recognition system aligns more closely with practical application requirements. Conceptualization, C.L., G.R. and Z.W. ; methodology, C.L. and G.R. ; software, G.R. ; validation, G.R. ; formal analysis, Z.W. ; investigation, G.R. ; resources, C.L. and Z.W. ; data curation, G.R. ; writing--original draft preparation, G.R. ; writing--review and editing, G.R. ; visualization, G.R. ; supervision, C.L. and Z.W. ; project administration, Z.W. ; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The National Health and Health Commission, Ministry of Education, Ministry of Science and Technology, and the State Administration of Traditional Chinese Medicine publicly released the 'Life Science and Medical Research Involving Human Beings' in February 2023, which stipulates that 'under the premise of using information data or biological samples of human beings, not causing harm to human beings, and not involving sensitive personal information or commercial interests. Ethical review may be waived for life sciences and medical research involving human beings in some cases'. Ethical approval was not necessary as the data for this study did not include any information that would identify individuals.