Paper Info
Title
Assessing Falling Risk in Elderly with the Ten Meter Walking Test: A Machine Learning Approach.
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. This paper introduces the sensing capabilities of the i-Walker and its use in measuring the 10MWT. The volunteers in this study are a subset the participants in the pilots installed during the I-DONT FALL EU funded project. This paper also proposes a Machine Learning method for analyzing individuals' walking ability and risk of falling by using an instrumented smart walker: the i-Walker.
Year
DOI
Venue
2016
10.3233/978-1-61499-696-5-227
Frontiers in Artificial Intelligence and Applications
Keywords
Field
DocType
Machine Learning,Assistive Technologies,Healthcare
Falling risk,Metre (music),Engineering,Operations management
Conference
Volume
ISSN
Citations 
288
0922-6389
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Atia Cortés121.52
Javier Béjar211.45
Cristian Barrué3348.16
Antonio B. Martínez Velasco4216.15
Ulises Cortés561998.84