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Open AccessArticle

Pervasive Lying Posture Tracking

1
Philips Research North America, Cambridge, MA 02141, USA
2
School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(20), 5953; https://doi.org/10.3390/s20205953
Received: 31 July 2020 / Revised: 14 October 2020 / Accepted: 18 October 2020 / Published: 21 October 2020
(This article belongs to the Special Issue Body Worn Sensors and Related Applications)
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 F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. 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. View Full-Text
Keywords: lying posture tracking; traditional machine learning; ensemble classification; deep recurrent neural network models; long short-term memory sequence classification model lying posture tracking; traditional machine learning; ensemble classification; deep recurrent neural network models; long short-term memory sequence classification model
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Alinia, P.; Samadani, A.; Milosevic, M.; Ghasemzadeh, H.; Parvaneh, S. Pervasive Lying Posture Tracking. Sensors 2020, 20, 5953.

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