@article{ref1,
title="A data-driven approach for detecting gait events during turning in people with Parkinson's disease and freezing of gait",
journal="Gait and posture",
year="2020",
author="Vanrumste, Bart and Slaets, Peter and Spildooren, Joke and D'cruz, Nicholas and Nieuwboer, Alice and Filtjens, Benjamin",
volume="80",
number="",
pages="130-136",
abstract="BACKGROUND: Manual annotation of initial contact (IC) and end contact (EC) is a time consuming process. There are currently no robust techniques available to automate this process for Parkinson's disease (PD) patients with freezing of gait (FOG).   OBJECTIVE: To determine the validity of a data-driven approach for automated gait event detection.   METHODS: 15 freezers were asked to complete several straight-line and 360 degree turning trials in a 3D gait laboratory during the off-period of their medication cycle. Trials that contained a freezing episode were indicated as freezing trials (FOG) and trials without a freezing episode were termed as functional gait (FG). Furthermore, the highly varied gait data between onset and termination of a FOG episode was excluded. A Temporal Convolutional Neural network (TCN) was trained end-to-end with lower extremity kinematics. A Bland-Altman analysis was performed to evaluate the agreement between the results of the proposed model and the manual annotations.   RESULTS: For FOG-trials, F1 scores of 0.995 and 0.992 were obtained for IC and EC, respectively. For FG-trials, F1 scores of 0.997 and 0.999 were obtained for IC and EC, respectively. The Bland-Altman plots indicated excellent timing agreement, with on average 39% and 47% of the model predictions occurring within 10 ms from the manual annotations for FOG-trials and FG-trials, respectively.   SIGNIFICANCE: These results indicate that our data-driven approach for detecting gait events in PD patients with FOG is sufficiently accurate and reliable for clinical applications.<p /> <p>Language: en</p>",
language="en",
issn="0966-6362",
doi="10.1016/j.gaitpost.2020.05.026",
url="http://dx.doi.org/10.1016/j.gaitpost.2020.05.026"
}