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AuthorQidwai, Uvais
AuthorAl-Sulaiti, Sara
AuthorAhmed, Ghadeer
AuthorHegazy, Asmaa
AuthorIlyas, Saadat Kamran
Available date2021-07-01T05:48:09Z
Publication Date2016
Publication NameIECBES 2016 - IEEE-EMBS Conference on Biomedical Engineering and Sciences
ResourceScopus
URIhttp://dx.doi.org/10.1109/IECBES.2016.7843512
URIhttp://hdl.handle.net/10576/20926
AbstractStroke patients, as well as all those patients who are bed-bound for a long period of time, are highly susceptible to preventable secondary complications such as pressure ulcers or commonly known as bedsores. Such secondary complications may lead to progression of symptoms and are an important cause of delayed hospital discharges or even fatalities. The unnecessary long occupancy of the bed causes stress on the hospital's operations as well and adds to the operational costs. Newer strategies are, therefore, urgently needed to improve detection of patients at risk and to prevent such complications. In this paper, a system has been presented for detection and quantification of patient's position as a measure of possible critical triggering scenario that can lead to the development of the pressure ulcers. This multidisciplinary work combines the clinical knowledge of physicians and scientists at the Stroke ward with the instrumentation and intelligent algorithm into an add-on system that can be easily attached to the existing beds in any typical neurological clinics as well as intensive care units (ICU). Data fusion is assessed continuously with intelligent algorithms to alert the medical staff about patient's physical condition, e.g.Turning to reduce the risk of post-stroke complications. The built device was tested with healthy volunteers and a detailed study is being conducted at the hospital to evaluate the clinical feasibility of the design.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBedsores
Image analysis
Load sensing
Posture detection
Pressure sensors
Pressure Ulcers
TitleIntelligent integrated instrumentation platform for monitoring long-Term bedridden patients
TypeConference Paper
Pagination561-564


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