{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:57:03Z","timestamp":1781535423068,"version":"3.54.5"},"reference-count":31,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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\u2019s pose, and locate and track high-risk regions over time so that necessary attention can be provided.<\/jats:p>","DOI":"10.3390\/s21134356","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T11:07:40Z","timestamp":1624619260000},"page":"4356","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring"],"prefix":"10.3390","volume":"21","author":[{"given":"Nasim","family":"Hajari","sequence":"first","affiliation":[{"name":"Multimedia Research Centre, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2737-7644","authenticated-orcid":false,"given":"Carlos","family":"Lastre-Dominguez","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Universidad de Guanajuato, Salamanca 36885, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chester","family":"Ho","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Alberta, Edmonton, AB T6G 2E8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oscar","family":"Ibarra-Manzano","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Universidad de Guanajuato, Salamanca 36885, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9699-4895","authenticated-orcid":false,"given":"Irene","family":"Cheng","sequence":"additional","affiliation":[{"name":"Multimedia Research Centre, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1109\/RBME.2019.2927200","article-title":"Pressure Injury Prevention: A Survey","volume":"13","author":"Mansfield","year":"2020","journal-title":"IEEE Rev. 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