Sleeping Postures

Introduction

Sleep is a dynamic and complicated brain process on which several crucial brain activities are dependent. Sleep plays an essential role in helping the brain, heart, and lungs perform at their best. Additionally,, sleep decreases the risk of chronic diseases and improves immunity. Sleeping postures have a positive or negative effect on the body.

Classification of Sleeping Posture

Common sleeping postures are classified into the following positions:

Supine Position

Lying on your back with your face pointing upwards is a common position maintained by Pilates practitioners to help align the body. When the pelvis and spine are aligned in a neutral position, this reduces the pressure on bones and ligaments. According to a 2019 study, supine sleeping posture may be helpful for lower back pain. However, professionals advise against sleeping supine during the final stages of pregnancy due to potential risks. Furthermore, lying on one's back could lead to snoring, sleep apnea or worsening GERD symptoms.

In a supine position, a person adopts a number of supine sleeping postures. such as:

• both hands positioned at the side (BHS; arms abducted 0°), or
• on the chest (BHC; arms abducted 45°), or
• the dominant hand on the forehead (DHF; dominant arm abducted 90°)

sleeping on your side

The majority of people sleep on their sides. There are a few advantages to side sleeping. as it helps in reducing heart burn and snoring. Furthermore, side sleeping is beneficial for pregnant women, back pain, sleep apnea and older people as it promotes healthy spinal alignment.

sleeping on your back

Lying on the back is the second most popular sleep position. Sleeping on the back can relieve allergies or stuffy nasal congestion.

sleeping on your stomach

Of all the sleeping positions, the stomach position provides the least support to the back and puts greater pressure on the spine, which could lead to pain. Wrinkles on the face could arise from stomach sleeping.It delays recovery in patients who have a history of spinal injury.

reported that for patients with Congestive Heart Failure (CHF), the amount of time spent in the right lateral position is significantly more than the amount of time spent in the left lateral position. This strategy might help them to avoid the discomfort caused by the enlarged apical heartbeat or further hemodynamic or autonomic compromise.

Effect of various postures on quality of sleep

It was observed that persons with poor sleep quality have more frequent posture changes.

A side-lying posture results in sleep-related musculoskeletal problems, such as neck and back pain.

Individuals with obstructive sleep apnea, supine, and prone postures may affect their respiration because of tongue and palate prolapse and pressure on the thorax.

In the supine positions, normal spinal curvature is to be maintained. In side-lying positions, the cervical and thoracic portions of the spine should align with each other so that there is no muscle stiffness and no excessive load imposed on the cervical facet joint.

e effect of body posture on sleep-related breathing abnormalities, we refer mainly to the worsening effect of the supine posture on these abnormalities and how the lateral, prone or sitting positions produce, in most of the cases, a significant improvement and in some cases even a total elimination of these disturbances.

Tools to detect Posture

Sleep assessment technologies like wearables, polysomnography, and unobtrusive intelligent sensors could track physiological indicators and sleep behaviours.

Depth-camera-based systems are commonly used for sleep monitoring, since they are noncontact, easier to maintain, allow for privacy, and can work well at night without visible light.

A support vector machine (SVM) from images acquired using a Kinect Artec scanner generates a high accuracy in classifying seven postures.

Sleep Posture Classification using Bed Sensor Data and Neural Networks

we use a hydraulic bed sensor designed for capturing ballistocardiogram (BCG) signals [15]. It is composed of a set of four water tubes each fitted with a pressure sensor (Fig. 1) which are placed under the bed mattress for the purpose of non-invasive heart motion measurement. The four-channel signal is sampled at 100 Hz and in its raw format, it contains a DC bias (the weight of the body lying on the bed). We simply ran moving averages to remove the high-frequency part of the signal. Variations in the DC values for the four channels are known to be correlated to the location of the person on the bed https://ieeexplore.ieee.org/abstract/document/8512436?casa_token=hoR3NNwHJ-EAAAAA:X9DWGXC1bR61hcKfe9Sho7lhVMC52xvwS_3_CBlEcupYv3qN6ig34XUkSfEoYWgK9YzPBMibWtc

Multimodal Sleeping Posture Classification

we focus on sleeping posture classification, which can be important not only for bedsore prevention [14], [17] but also for sleeping quality analysis https://ieeexplore.ieee.org/abstract/document/5597772?casa_token=-aeycxe8QUIAAAAA:wXpJVdyJ8dRebI4Ep7S_3N2y5PETnvieYqAsuAiLZOSovKdzc78YFb5vvsmYaNHFW6OL25cf_qU

Sleep posture classification with multi-stream CNN using vertical distance map

Sleep posture type was also considered in the study. Sleep Assessment and Advisory Service (SAAS) clusters sleep postures into six types: fetus, log, yearner, soldier, freefaller, and starfish. Most studies only focused on the identification of three sleep postures, including supine, left side, and right side incorrect sleep positions held for a considerably long duration can result in spinal alignment problems.

An HMM-based method was applied for analyzing posture changes and myoclonic twitches. However, wearable device usually cause uncomfortable sleep, and that may affect sleep quality of people

Pressure mattress was also widely used for sleep posture recognition. Jason et al. [11] monitored sleep postures using a pressure-sensitive bed sheet, and features of pressure images were extracted for identifying sleep postures using three sparse classifiers https://ieeexplore.ieee.org/abstract/document/8369761?casa_token=5NmOhTNhi8UAAAAA:p3p74V5UwUHFRFmezrzbzJ2fQkvWWDUIfvquoxqd-UmOOGSnTnPzkPyj2JSrOOISPLZcrgYgJjI

we propose to enhance the accuracy of the under-blanket sleep posture classification with a new deep learning model architecture with a guided input of anatomical features generated using a pose estimator [39]. The classification covers seven common sleep postures, including the supine posture, prone postures with the head turned left and right, left- and right-sided log postures, and left- and right-sided fetal postures. The key contributions of this study are as follows:

• We developed a posture classification system that can be generalized to various blanket conditions.
• We proposed an integrative innovation for the deep learning model to improve the classification performance through anatomical landmark features generated using a pose estimator https://www.mdpi.com/1660-4601/19/20/13491