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المؤلفQidwai, Uvais
المؤلفAl-Sulaiti, Sara
المؤلفAhmed, Ghadeer
المؤلفHegazy, Asmaa
المؤلفIlyas, Saadat Kamran
تاريخ الإتاحة2021-07-01T05:48:09Z
تاريخ النشر2016
اسم المنشورIECBES 2016 - IEEE-EMBS Conference on Biomedical Engineering and Sciences
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/IECBES.2016.7843512
معرّف المصادر الموحدhttp://hdl.handle.net/10576/20926
الملخصStroke 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعBedsores
Image analysis
Load sensing
Posture detection
Pressure sensors
Pressure Ulcers
العنوانIntelligent integrated instrumentation platform for monitoring long-Term bedridden patients
النوعConference Paper
الصفحات561-564


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