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Comparing machine learning approaches for fall risk assessment

: Silva, Joana; Madureira, Joao; Tonelo, Claudia; Baltazar, Daniela; Silva, Catarina; Martins, Anabela Correia; Alcobia, Carlos; Sousa, Ines


Maciel, C.D. ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
BIOSTEC 2017, 10th International Joint Conference on Biomedical Engineering Systems and Technologies. Proceedings. Vol.4: Biosignals : Porto, Portugal, February 21-23, 2017
SciTePress, 2017
ISBN: 978-989-758-212-7
International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) <10, 2017, Porto>
International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS) <10, 2017, Porto>
European Commission EC
Fraunhofer AICOS ()
fall risk assessment; inertial sensors; pressure Platform; Sit-to-Stand; 4-Stage Test; machine learning; classification; regression

Traditional 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.