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

TL;DR

This study presents a hybrid ensemble machine learning approach that accurately classifies resting positions using physiological signals, offering a low-cost, privacy-preserving alternative to traditional sleep monitoring methods.

Contribution

The paper introduces a novel stacked ensemble model combining Decision Tree, Random Forest, and XGBoost to classify resting postures with 100% accuracy based on physiological data.

Findings

Achieved 100% accuracy in classifying resting positions.

Demonstrated the feasibility of using physiological signals for posture monitoring.

Proposed a wearable-compatible, low-cost solution for sleep and posture assessment.

Abstract

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.