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Assessing falling risk in elderly with the ten meter walking test: A machine learning approach

Author
Cortes, A.; Bejar, J.; Barrue, C.; Martinez, A.; Cortes, U.
Type of activity
Presentation of work at congresses
Name of edition
19th International Conference of the Catalan Association for Artificial Intelligence
Date of publication
2016
Presentation's date
2016-10
Book of congress proceedings
Artificial Intelligence Research and Development: proceedings of the 19th International Conference of the Catalan Association for Artificial Intelligence, Barcelona, Catalonia, Spain, October 19-21, 2016
First page
227
Last page
232
DOI
10.3233/978-1-61499-696-5-227
URL
http://ebooks.iospress.nl/volumearticle/45334 Open in new window
Abstract
The Ten Meter Walking Test (10MWT) has been widely used in rehabilitation literature as an indicator of physical decline and other health-related outcomes. With an increasing senior population, it is important to analyze and estimate physical limitations in older people to prevent falls and their consequences, not only for the individual's benefit but also for their social environment and the sustainability of public health-care systems. The 10MWT as measured today gives only values of speed. Th...
Keywords
Artificial intelligence, Assistive technology, Biped locomotion, Falling risks, Health care, Machine learning approaches, Machine learning methods, Physical limitations, Public health, Risk assessment, Smart walkers, Social environment, Sustainable development, Walking ability
Group of research
CREB - Biomedical Engineering Research Centre
GRINS - Intelligent Robots and Systems
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

Participants