: Received: 31 July 2020 / Approved: 2 August 2020 / Online: 2 August 2020 (12:12:06 CEST)
: Received: 18 October 2020 / Approved: 19 October 2020 / Online: 19 October 2020 (10:57:42 CEST)
Alinia, P.; Samadani, A.; Milosevic, M.; Ghasemzadeh, H.; Parvaneh, S. Pervasive Lying Posture Tracking. Sensors2020, 20, 5953.
Alinia, P.; Samadani, A.; Milosevic, M.; Ghasemzadeh, H.; Parvaneh, S. Pervasive Lying Posture Tracking. Sensors 2020, 20, 5953.
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 about 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 to design 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 most effective in 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 F-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.
lying posture tracking; traditional machine learning; ensemble classification; deep neural network models; long short-term memory sequence classification model
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