@article{ref1,
title="Walking behavior change detector for a "smart" walker",
journal="Procedia computer science",
year="2014",
author="Weiss, Viviana and Bologna, Guido and Cloix, Séverine and Hasler, David and Pun, Thierry",
volume="39",
number="",
pages="43-50",
abstract="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.<p />",
language="en",
issn="1877-0509",
doi="10.1016/j.procs.2014.11.008",
url="http://dx.doi.org/10.1016/j.procs.2014.11.008"
}