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Alinia P, Samadani A, Milosevic M, et al. Pervasive Lying Posture Tracking. Sensors (Basel). 2020;20(20)doi: 10.3390/s20205953.
Alinia, P., Samadani, A., Milosevic, M., Ghasemzadeh, H., & Parvaneh, S. (2020). Pervasive Lying Posture Tracking. Sensors (Basel, Switzerland), 20(20), . https://doi.org/10.3390/s20205953
Alinia, Parastoo, et al. "Pervasive Lying Posture Tracking." Sensors (Basel, Switzerland) vol. 20,20 (2020). doi: https://doi.org/10.3390/s20205953
Alinia P, Samadani A, Milosevic M, Ghasemzadeh H, Parvaneh S. Pervasive Lying Posture Tracking. Sensors (Basel). 2020 Oct 21;20(20). doi: 10.3390/s20205953. PMID: 33096769; PMCID: PMC7589361.
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Sensors (Basel). 2020 Oct 21;20(20). doi: 10.3390/s20205953.

Pervasive Lying Posture Tracking.

Sensors (Basel, Switzerland)

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

Affiliations

  1. Philips Research North America, Cambridge, MA 02141, USA.
  2. School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA.

PMID: 33096769 PMCID: PMC7589361 DOI: 10.3390/s20205953

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 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.

Keywords: deep recurrent neural network models; ensemble classification; long short-term memory sequence classification model; lying posture tracking; traditional machine learning

References

  1. IEEE J Biomed Health Inform. 2013 Nov;17(6):985-93 - PubMed
  2. Telemed J E Health. 2011 Apr;17(3):177-84 - PubMed
  3. J Neurol Neurosurg Psychiatry. 1999 Oct;67(4):439-44 - PubMed
  4. Gerontologist. 2020 Jul 15;60(5):926-934 - PubMed
  5. Nurs Manag (Harrow). 2014 May;21(2):18-21 - PubMed
  6. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5254-7 - PubMed
  7. J Nutr Health Aging. 2009 Jun;13(6):576-83 - PubMed
  8. Chest. 2012 May;141(5):1337-1345 - PubMed
  9. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7175-8 - PubMed
  10. J Hosp Med. 2016 May;11(5):341-7 - PubMed
  11. NPJ Digit Med. 2020 Mar 13;3:37 - PubMed
  12. Scand J Caring Sci. 2004 Mar;18(1):57-64 - PubMed
  13. J Clin Sleep Med. 2017 Nov 15;13(11):1301-1310 - PubMed
  14. Eur J Med Res. 2018 May 30;23(1):28 - PubMed
  15. PLoS One. 2012;7(10):e48089 - PubMed
  16. Sensors (Basel). 2010;10(2):1154-75 - PubMed
  17. Lancet. 1991 May 25;337(8752):1244-7 - PubMed
  18. Nat Sci Sleep. 2017 Nov 01;9:267-275 - PubMed
  19. Chest. 2005 Oct;128(4):2130-7 - PubMed
  20. Am J Med. 2016 Aug;129(8):866-871.e1 - PubMed

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