Affiliations: [a] Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| [b] Department of Computer Science and Engineering, Seoul National University
Correspondence:
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Correspondence to: Beomseok Jeon, MD, PhD, Department of Neurology, College of Medicine, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul 110-774, Korea. Tel.: +82 2 2072 2876; E-mail: [email protected] and Jehee Lee, PhD, Department of Computer Science and Engineering, Seoul National University, 1 Gwanangno, Gwanak-gu, Seoul 08826, Korea. Tel.: +82 2 880 1845; E-mail: [email protected].
Note: [1] These authors contributed equally to this work.
Abstract: Background:Clinician-based rating scales or questionnaires for gait in Parkinson’s disease (PD) are subjective and sensor-based analysis is limited in accessibility. Objective:To develop an easily accessible and objective tool to evaluate gait in PD patients, we analyzed gait from a single 2-dimensional (2D) video. Methods:We prospectively recorded 2D videos of PD patients (n = 16) and healthy controls (n = 15) performing the timed up and go test (TUG). The gait was simultaneously evaluated with a pressure-sensor (GAITRite). We estimated the 3D position of toes and heels with a deep-learning based pose-estimation algorithm and calculated gait parameters including step length, step length variability, gait velocity and step cadence which was validated with the result from the GAITRite. We further calculated the time and steps required for turning. Then, we applied the algorithm to previously recorded and archived videos of PD patients (n = 32) performing the TUG. Results:From the validation experiment, gait parameters derived from video tracking were in excellent agreement with the parameters obtained with the GAITRite. (Intraclass correlation coefficient > 0.9). From the analysis with the archived videos, step length, gait velocity, number of steps, and the time required for turning were significantly correlated (Absolute R > 0.4, p < 0.005) with the Freezing of gait questionnaire, Unified PD Rating scale part III total score, HY stage and postural instability. Furthermore, the video-based tracking objectively measured significant improvement of step length, gait velocity, steps and the time required for turning with antiparkinsonian medication. Conclusion:2D video-based tracking could objectively evaluate gait in PD patients.
Keywords: Parkinson disease, gait, deep-learning, video tracking
