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Quantitative gait analysis using a pose-estimation algorithm with a single 2D-video of PD patients

JH. Shin, R. Yu, JN. Ong, CY. Lee, SH. Jeon, HP. Park, HJ. Kim, JH. Lee, B. Jeon (Seoul, Republic of Korea)

Meeting: MDS Virtual Congress 2021

Abstract Number: 1315

Keywords: Gait disorders: Clinical features, Parkinsonism

Category: Technology

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.

Background: Clinician based rating scales or questionnaires for gait in Parkinson’s disease (PD) are subjective and sensor-based analysis is limited in accessibility

Method: 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 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. Steps required for turning was validated with manual measure. Then, we applied the algorithm to archieved 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 archieved 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: The video-based gait analysis can reliably and objectively measure gait parameters with 2D videos of PD patients. This study quantitatively analyzed the gait of PD patients with a “marker-less” and “automated” algorithm using 2D video. Quantitative gait analysis using accessible and cost-effective 2D videos has the potential to be widely used in daily practice as an objective measurement in research and clinical practice.

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To cite this abstract in AMA style:

JH. Shin, R. Yu, JN. Ong, CY. Lee, SH. Jeon, HP. Park, HJ. Kim, JH. Lee, B. Jeon. Quantitative gait analysis using a pose-estimation algorithm with a single 2D-video of PD patients [abstract]. Mov Disord. 2021; 36 (suppl 1). https://www.mdsabstracts.org/abstract/quantitative-gait-analysis-using-a-pose-estimation-algorithm-with-a-single-2d-video-of-pd-patients/. Accessed May 21, 2025.
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