@article{ref1,
title="Online learning of gait models for calculation of gait parameters",
journal="Conference proceedings - IEEE engineering in medicine and biology society",
year="2016",
author="Waugh, Jamie L. S. and Trinh, Anton and Mohammed, Ryan R. and McIlroy, William E. and Kulic, Dana and Waugh, Jamie L. S. and Trinh, Anton and Mohammed, Ryan R. and McIlroy, William E. and Kulic, Dana and Waugh, Jamie L. S. and Kulic, Dana and Trinh, Anton and McIlroy, William E. and Mohammed, Ryan R.",
volume="2016",
number="",
pages="6146-6149",
abstract="This paper proposes a novel approach for gait analysis from wearable sensing, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter and intra-personal variability by creating an individualized model. Once the algorithm has converged to the input signal, key gait events can be identified relative to the estimated gait phase; these events can then be used to calculate gait parameters. The approach is implemented and tested on a human motion dataset where heel impact and toe takeoff events are extracted with an average error of 0.04 cycles.<p /> <p>Language: en</p>",
language="en",
issn="1557-170X",
doi="10.1109/EMBC.2016.7592131",
url="http://dx.doi.org/10.1109/EMBC.2016.7592131"
}