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Smart Health System to Detect Dementia Disorders Using Virtual Reality
Smart Health System to Detect Dementia Disorders Using Virtual Reality
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Smart Health System to Detect Dementia Disorders Using Virtual Reality
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Smart Health System to Detect Dementia Disorders Using Virtual Reality
Smart Health System to Detect Dementia Disorders Using Virtual Reality

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Smart Health System to Detect Dementia Disorders Using Virtual Reality
Smart Health System to Detect Dementia Disorders Using Virtual Reality
Journal Article

Smart Health System to Detect Dementia Disorders Using Virtual Reality

2021
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Overview
Smart health technology includes physical sensors, intelligent sensors, and output advice to help monitor patients’ health and adjust their behavior. Virtual reality (VR) plays an increasingly larger role to improve health outcomes, being used in a variety of medical specialties including robotic surgery, diagnosis of some difficult diseases, and virtual reality pain distraction for severe burn patients. Smart VR health technology acts as a decision support system in the diseases diagnostic test of patients as they perform real world tasks in virtual reality (e.g., navigation). In this study, a non-invasive, cognitive computerized test based on 3D virtual environments for detecting the main symptoms of dementia (memory loss, visuospatial defects, and spatial navigation) is proposed. In a recent study, the system was tested on 115 real patients of which thirty had a dementia, sixty-five were cognitively healthy, and twenty had a mild cognitive impairment (MCI). The performance of the VR system was compared with Mini-Cog test, where the latter is used to measure cognitive impaired patients in the traditional diagnosis system at the clinic. It was observed that visuospatial and memory recall scores in both clinical diagnosis and VR system of dementia patients were less than those of MCI patients, and the scores of MCI patients were less than those of the control group. Furthermore, there is a perfect agreement between the standard methods in functional evaluation and navigational ability in our system where P-value in weighted Kappa statistic= 100% and between Mini-Cog-clinical diagnosis vs. VR scores where P-value in weighted Kappa statistic= 93%.