# Sleep Posture Recognition Method Based on Sparse Body Pressure Features
## 1 Introduction  
Sleep health is intricately linked to individuals' everyday activities and their physical and mental well-being. The restorative and regulatory functions of the human body during sleep sustain its regular operation [1]. The sleeping position significantly influences sleep quality and physiological functions by affecting breathing, blood circulation, and musculoskeletal pressure [2, 3, 4]. Prevalent sleeping postures encompass supine, lateral, and prone positions [5]. The supine position facilitates airway patency, thereby diminishing the likelihood of sleep apnea and hypoventilation syndrome; however, it is contraindicated for individuals with gastroesophageal reflux disease [6]. The lateral position benefits cardiac function, particularly in patients with cardiovascular conditions, yet prolonged maintenance on the same side may result in scoliosis and muscular strain [7]. Conversely, the prone position, while providing a sense of security, exerts pressure on the chest and abdomen, adversely impacting respiratory and circulatory functions [8].  
Impaired sleep quality or sleep disorders adversely impact life and are linked to significant health issues, including pressure ulcers [9], mental illnesses [10], and cardiovascular [11] and cerebrovascular diseases [12]. Accurate identification of sleeping postures is crucial for enhancing sleep quality and preventing illness, making the advancement of effective monitoring and recognition systems a significant focus in sleep health.  
The main results of the work in this paper are as follows:  
1. A model of an airbag mattress that can recognize sleeping posture and modify its softness and stiffness at numerous spots is offered. The methods for recognizing sleeping positions and adjusting firmness utilize identical hardware configurations, hence diminishing system complexity.
2. On the basis of using entropy-weighted scores to filter base learners, the complementarity scores between models are combined to filter two sets of base classifier combinations. These two approaches can greatly enhance the complementary nature of the model in ensemble learning, where selected combinations of base classifiers and convolutional neural networks (CNNs) are used together to build the base learning layer of the model. Among other things, CNNs can explore deep relationships between features.
3. An ensemble learning model combining voting and stacking was developed. It can not only further extract data features based on the foundational model, thus diminishing the bias of an individual model, but also streamline the model's structure with hyperparameters optimized for each base model utilizing the Ivy optimization algorithm to enhance model performance.  
The rest of this article is structured as follows. Section 2 reviews the pertinent literature; Section 3 delineates the systematic design of the mattress prototype; Section 4 elaborates on the methodology employed during the experiments, encompassing data collection, processing, model construction, and hyperparameter optimization; Section 5 evaluates the experimental results and juxtaposes them with other related methodologies; and Section 6 concludes the study.