Walking behavior change detector for a “smart” walker

Weiss, Viviana (Computer Science Department, University of Geneva, Geneva, Switzerland) ; Bologna, Guido (Computer Science Department, University of Geneva, Geneva, Switzerland) ; Cloix, Séverine (Computer Science Department, University of Geneva, Geneva, Switzerland ; CSEM SA, Neuchâtel, Switzerland)

This study investigates the design of a novel real-time system to detect walking behavior changes using an accelerometer on a rollator. No sensor is required on the user. We propose a new non-invasive approach to detect walking behavior based on the motion transfer by the user on the walker. Our method has two main steps; the first is to extract a gait feature vector by analyzing the three-axis accelerometer data in terms of magnitude, gait cycle and frequency. The second is to classify gait with the use of a decision tree of multilayer perceptrons. To assess the performance of our technique, we evaluated different sampling window lengths of 1, 3 an 5 seconds and four different Neural Network architectures. The results revealed that the algorithm can distinguish walking behavior such as normal, slow and fast with an accuracy of about 86%. This research study is part of a project aiming at providing a simple and non-invasive walking behavior detector for elderly who use rollators.


Note: BOLOGNA, Guido est un chercheur à la HES-SO, HEPIA, depuis 2011.


Keywords:
Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEPIA - Genève
Institute:
inIT - Institut d'Ingénierie Informatique et des Télécommunications
Date:
2014-01
Pagination:
8 p.
Published in:
Procedia Computer Science
Numeration (vol. no.):
2014, vol. 39, pp. 43-50
DOI:
ISSN:
1877-0509
Appears in Collection:



 Record created 2020-07-10, last modified 2020-07-17

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