Silva, JoanaJoanaSilvaMadureira, JoaoJoaoMadureiraTonelo, ClaudiaClaudiaToneloBaltazar, DanielaDanielaBaltazarSilva, CatarinaCatarinaSilvaMartins, Anabela CorreiaAnabela CorreiaMartinsAlcobia, CarlosCarlosAlcobiaSousa, InesInesSousa2022-03-132022-03-132017https://publica.fraunhofer.de/handle/publica/39969610.5220/00062278022302302-s2.0-85051701322Traditional fall risk assessment tests are based on timing certain physical tasks, such as the timed up and go test, counting the number of repetitions in a certain time-frame, as the 30-second sit-to-stand or observation such as the 4-stage balance test. A systematic comparison of multifactorial assessment tools and their instrumentation for fall risk classification based on machine learning approaches were studied for a population of 296 community-dwelling older persons aged above 50 years old. Using features from inertial sensors and a pressure platform by opposition to using solely the tests scores and personal metrics increased the F-Score of Naive Bayes classifier from 72.85% to 92.61%. Functional abilities revealed higher association with fall level than personal conditions such as gender, age and health conditions.enfall risk assessmentinertial sensorspressure PlatformSit-to-Stand4-Stage Testmachine learningclassificationregressionconference paper