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Votre centre de documentation sera exceptionnellement fermé de 12h30 à 13h ce lundi 18 novembre.
Egalement, il sera fermé de 12h30 à 13h30 ce mercredi 20 novembre.
Lundi : 8h-18h30
Mardi : 8h-17h30
Mercredi 9h-16h30
Jeudi : 8h30-18h30
Vendredi : 8h30-12h30 et 13h-14h30
Votre centre de documentation sera exceptionnellement fermé de 12h30 à 13h ce lundi 18 novembre.
Egalement, il sera fermé de 12h30 à 13h30 ce mercredi 20 novembre.
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Auteur Dylan Fievez |
Documents disponibles écrits par cet auteur
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Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People / Fabien Buisseret
Titre : Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People Type de document : document électronique Auteurs : Fabien Buisseret ; Louis Catinus ; Remi Grenard ; L. Jojczyk ; Dylan Fievez ; Vincent Barvaux ; Frédéric Dierick Année de publication : 2020 Langues : Français (fre) Résumé : Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants. En ligne : https://luck.synhera.be/bitstream/handle/123456789/271/sensors-20-03207.pdf?sequ [...] Permalink : ./index.php?lvl=notice_display&id=98163 Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People [document électronique] / Fabien Buisseret ; Louis Catinus ; Remi Grenard ; L. Jojczyk ; Dylan Fievez ; Vincent Barvaux ; Frédéric Dierick . - 2020.
Langues : Français (fre)
Résumé : Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants. En ligne : https://luck.synhera.be/bitstream/handle/123456789/271/sensors-20-03207.pdf?sequ [...] Permalink : ./index.php?lvl=notice_display&id=98163 Exemplaires
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