A data-driven approach for detecting gait events during turning in people with Parkinson's disease and freezing of gait.
Benjamin Filtjens (Katholieke Universiteit Leuven)
Alice Nieuwboer (Katholieke Universiteit Leuven)
Nicholas D'Cruz (Katholieke Universiteit Leuven)
Joke Spildooren (Katholieke Universiteit Leuven)
Peter Slaets (Katholieke Universiteit Leuven)
Bart Vanrumste (Katholieke Universiteit Leuven)
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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.