  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.
