标题： 基于使用堆叠集成学习的EGG、ECG、呼吸频率和SpO2确定静息位置 显示英文标题

标题： Identification of the Resting Position Based on EGG, ECG, Respiration Rate and SpO2 Using Stacked Ensemble Learning

摘要： 休息对于高水平的生理和心理表现至关重要。 肌肉也需要休息来修复、重建和增强。 休息质量和休息姿势之间有显著的相关性。 因此，识别休息姿势对保持健康生活至关重要。 休息姿势可以分为四种基本类别：仰卧（仰卧位）、左侧/右侧卧位和自由下落姿势。 后来的姿势已经被研究人员模棱两可地认为是不健康的姿势，因此可以被排除。 在本文中，我们基于从生理参数收集的数据分析了其他三种休息姿势： 胃电图（EGG）、心电图（ECG）、呼吸频率、心率和血氧饱和度（SpO2）。 基于这些参数，使用结合决策树、随机森林和Xgboost算法设计的混合堆叠集成机器学习模型对休息姿势进行分类。 我们的研究表明，使用混合模型可以100%准确地预测休息姿势。 基于生理参数识别休息姿势的方法有望集成到可穿戴设备中。 这是一种低成本、高精度且自主的技术，在保持用户隐私的同时，通过消除传统用于进行多导睡眠监测（睡眠监测）或休息姿势研究的RGB摄像头的使用来监测身体姿势。 显示英文摘要

摘要： Rest is essential for a high-level physiological and psychological performance. It is also necessary for the muscles to repair, rebuild, and strengthen. There is a significant correlation between the quality of rest and the resting posture. Therefore, identification of the resting position is of paramount importance to maintain a healthy life. Resting postures can be classified into four basic categories: Lying on the back (supine), facing of the left / right sides and free-fall position. The later position is already considered to be an unhealthy posture by researchers equivocally and hence can be eliminated. In this paper, we analyzed the other three states of resting position based on the data collected from the physiological parameters: Electrogastrogram (EGG), Electrocardiogram (ECG), Respiration Rate, Heart Rate, and Oxygen Saturation (SpO2). Based on these parameters, the resting position is classified using a hybrid stacked ensemble machine learning model designed using the Decision tree, Random Forest, and Xgboost algorithms. Our study demonstrates a 100% accurate prediction of the resting position using the hybrid model. The proposed method of identifying the resting position based on physiological parameters has the potential to be integrated into wearable devices. This is a low cost, highly accurate and autonomous technique to monitor the body posture while maintaining the user privacy by eliminating the use of RGB camera conventionally used to conduct the polysomnography (sleep Monitoring) or resting position studies.