Accuracy and Reliability of Onset Detection Algorithms in Gait Initiation for Healthy Controls and Participants With Parkinson’s Disease

in Journal of Applied Biomechanics
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Accurate and reliable detection of the onset of gait initiation is essential for the correct assessment of gait. Thus, this study was aimed at evaluation of the reliability and accuracy of 3 different center of pressure–based gait onset detection algorithms: A displacement baseline–based algorithm (method 1), a velocity baseline–based algorithm (method 2), and a velocity extrema–based algorithm (method 3). The center of pressure signal was obtained during 10 gait initiation trials from 16 healthy participants and 3 participants with Parkinson’s disease. Intrasession and absolute reliability of each algorithm was assessed using the intraclass correlation coefficient and the coefficient of variation of center of pressure displacement during the postural phase of gait initiation. The accuracy was evaluated using the time error of the detected onset by each algorithm relative to that of visual inspection. The authors’ results revealed that although all 3 algorithms had high to very high intrasession reliabilities in both healthy subjects and subjects with Parkinson’s disease, methods 2 and 3 showed significantly better absolute reliability than method 1 in healthy controls (P = .001). Furthermore, method 2 outperformed the other 2 algorithms in both healthy subjects and subjects with Parkinson’s disease with an overall accuracy of 0.80. Based on these results, the authors recommend using method 2 for accurate and reliable gait onset detection.

Chen and Asgari are with the Department of Computer Engineering & Computer Science, California State University Long Beach, Long Beach, CA; and the Department of Biomedical Engineering, California State University Long Beach, Long Beach, CA. Selvaraj is with the Department of Mechanical & Aerospace Engineering, California State University Long Beach, Long Beach, CA. Krishnan is with the Department of Physical Therapy, California State University Long Beach, Long Beach, CA.

Asgari (shadnaz.asgari@csulb.edu) is corresponding author.
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