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Citation
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Filtjens B, Nieuwboer A, D'cruz N, Spildooren J, Slaets P, Vanrumste B. Gait Posture 2020; 80: 130-136. |
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Abstract
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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.
Language: en |