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Article

Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring

1
Multimedia Research Centre, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
2
Department of Electronics Engineering, Universidad de Guanajuato, Salamanca 36885, Mexico
3
Department of Medicine, University of Alberta, Edmonton, AB T6G 2E8, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Silvia Fantozzi
Sensors 2021, 21(13), 4356; https://doi.org/10.3390/s21134356
Received: 13 May 2021 / Revised: 21 June 2021 / Accepted: 22 June 2021 / Published: 25 June 2021
(This article belongs to the Special Issue Perception and Intelligence Driven Sensing to Monitor Personal Health)
Pressure injury (PI) is a major problem for patients that are bound to a wheelchair or bed, such as seniors or people with spinal cord injuries. This condition can be life threatening in its later stages. It can be very costly to the healthcare system as well. Fortunately with proper monitoring and assessment, PI development can be prevented. The major factor that causes PI is prolonged interface pressure between the body and the support surface. A possible solution to reduce the chance of developing PI is changing the patient’s in-bed pose at appropriate times. Monitoring in-bed pressure can help healthcare providers to locate high-pressure areas, and remove or minimize pressure on those regions. The current clinical method of interface pressure monitoring is limited by periodic snapshot assessments, without longitudinal measurements and analysis. In this paper we propose a pressure signal analysis pipeline to automatically eliminate external artefacts from pressure data, estimate a person’s pose, and locate and track high-risk regions over time so that necessary attention can be provided. View Full-Text
Keywords: pressure injury; in-bed pose estimation; signal filtering and analysis; pressure tracking pressure injury; in-bed pose estimation; signal filtering and analysis; pressure tracking
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MDPI and ACS Style

Hajari, N.; Lastre-Dominguez, C.; Ho, C.; Ibarra-Manzano, O.; Cheng, I. Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring. Sensors 2021, 21, 4356. https://doi.org/10.3390/s21134356

AMA Style

Hajari N, Lastre-Dominguez C, Ho C, Ibarra-Manzano O, Cheng I. Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring. Sensors. 2021; 21(13):4356. https://doi.org/10.3390/s21134356

Chicago/Turabian Style

Hajari, Nasim, Carlos Lastre-Dominguez, Chester Ho, Oscar Ibarra-Manzano, and Irene Cheng. 2021. "Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring" Sensors 21, no. 13: 4356. https://doi.org/10.3390/s21134356

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