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.
journal_name
Gait Posturejournal_title
Gait & postureauthors
Filtjens B,Nieuwboer A,D'cruz N,Spildooren J,Slaets P,Vanrumste Bdoi
10.1016/j.gaitpost.2020.05.026subject
Has Abstractpub_date
2020-07-01 00:00:00pages
130-136eissn
0966-6362issn
1879-2219pii
S0966-6362(20)30176-4journal_volume
80pub_type
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