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• W2777455874 abstract "Fall prevention is a human, economic and social issue. The Timed Up and Go (TUG) test is widely used to identify individuals with a high fall risk. However, this test has been criticized because its “diagnostic” is too dependent on the conditions in which it is performed and on the healthcare professionals running it. We used the Microsoft Kinect ambient sensor to automate this test in order to reduce the subjectivity of outcome measures and to provide additional information about patient performance. Each phase of the TUG test was automatically identified from the depth images of the Kinect. Our algorithms accurately measured and assessed the elements usually measured by healthcare professionals. Specifically, average TUG test durations provided by our system differed by only 0.001 s from those measured by clinicians. In addition, our system automatically extracted several additional parameters that allowed us to accurately discriminate low and high fall risk individuals. These additional parameters notably related to the gait and turn pattern, the sitting position and the duration of each phase. Coupling our algorithms to the Kinect ambient sensor can therefore reliably be used to automate the TUG test and perform a more objective, robust and detailed assessment of fall risk." @default.
• W2777455874 created "2018-01-05" @default.
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• W2777455874 date "2017-12-22" @default.
• W2777455874 modified "2023-10-12" @default.
• W2777455874 title "Automating the Timed Up and Go Test Using a Depth Camera" @default.
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• W2777455874 doi "https://doi.org/10.3390/s18010014" @default.
• W2777455874 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5796464" @default.
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