Pervasive Lying Posture Tracking.

Parastoo Alinia,Hassan Ghasemzadeh,Mladen Milosevic,Saman Parvaneh,Ali Samadani

doi:10.3390/s20205953

Parastoo Alinia, Hassan Ghasemzadeh + Show 3 more

Open Access

https://doi.org/10.3390/s20205953

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Abstract

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an score that ranges from % to % with a coefficient of variation from to . The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

Highlights

Keeping track of in-bed lying postures and the transitions between them provide useful clinical information regarding the patients’ mobility [1,2], risk of developing hospital-acquired pressure injuries [3], hidden death in epilepsy [4], experiencing obstructive sleep apnea, circadian rhythm disorders [5], risk of sudden infant death [6], and quality of sleep [7]
We develop a traditional machine learning (ML) model for lying posture detection while using a single accelerometer sensor, which is is an ensemble of decision tree classifiers with hand-engineered time-domain features
We further perform a coefficient of variation (CoV) analysis in order to compare the performance of the models against the state-of-the-art

Summary

Introduction

Keeping track of in-bed lying postures and the transitions between them provide useful clinical information regarding the patients’ mobility [1,2], risk of developing hospital-acquired pressure injuries [3], hidden death in epilepsy [4], experiencing obstructive sleep apnea, circadian rhythm disorders [5], risk of sudden infant death [6], and quality of sleep [7]. Other works have utilized multiple wearable sensors on different body locations for continuous lying-posture detection, which imposes discomfort to the end-users and impedes long-term monitoring To address these issues, we develop a traditional machine learning (ML) model for lying posture detection while using a single accelerometer sensor, which is is an ensemble of decision tree classifiers with hand-engineered time-domain features. A study in [12] conducted by Kwasnicki et al proposed a lightweight sensing platform for monitoring sleep quality and posture using three wearable accelerometer sensors that were placed on both arms and the chest They applied a K-nearest neighbor, naive Bayes, and decision tree classifiers on the mean and variance of each axis of the signal from all three accelerometer sensors.

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Discussion

Conclusion