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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

dc.rights.licenseCC1en_US
dc.contributor.authorBUISSERET, Fabien
dc.contributor.authorCatinus, Louis
dc.contributor.authorGrenard, Remi
dc.contributor.authorJOJCZYK, Laurent
dc.contributor.authorFievez, Dylan
dc.contributor.authorBarvaux, Vincent
dc.contributor.authorDIERICK, Frédéric
dc.date.accessioned2020-06-05T11:57:56Z
dc.date.available2020-06-05T11:57:56Z
dc.date.issued2020-06-05
dc.identifier.urihttps://luck.synhera.be/handle/123456789/271
dc.identifier.doi10.3390/s20113207en_US
dc.description.abstractAssessing 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_US
dc.description.sponsorshipNoneen_US
dc.language.isoENen_US
dc.publisherMDPIen_US
dc.relation.ispartofSensorsen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.fren_US
dc.subjectRisk of fall, IMU, Neural networksen_US
dc.titleTimed 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 Peopleen_US
dc.typeArticle scientifiqueen_US
synhera.classificationSciences de la santé humaineen_US
synhera.institutionCeREF Techniqueen_US
synhera.otherinstitutionUMONSen_US
synhera.otherinstitutionRehazenteren_US
synhera.otherinstitutionUCLen_US
synhera.cost.total1 870.16en_US
synhera.cost.apc1 870.16en_US
synhera.cost.comp0en_US
synhera.cost.acccomp0en_US
dc.description.versionOuien_US
dc.rights.holderMDPIen_US


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