Automatic Measurement of Postural Abnormalities With a Pose Estimation Algorithm in Parkinson’s Disease

Objective This study aims to develop an automated and objective tool to evaluate postural abnormalities in Parkinson’s disease (PD) patients.Methods We applied a deep learning-based pose-estimation algorithm to lateral photos of prospectively enrolled PD patients (n = 28). We automatically measured...

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Bibliographic Details
Published in:Journal of movement disorders 2022, 15(2), , pp.140-145
Main Authors: Shin, Jung Hwan, Woo, Kyung Ah, Lee, Chan Young, Jeon, Seung Ho, Kim, Han-Joon, Jeon, Beomseok
Format: Article
Language:English
Subjects:
Brief Communication
camptocormia
parkinson’s disease
pose estimation
신경과학
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Summary:Objective This study aims to develop an automated and objective tool to evaluate postural abnormalities in Parkinson’s disease (PD) patients.Methods We applied a deep learning-based pose-estimation algorithm to lateral photos of prospectively enrolled PD patients (n = 28). We automatically measured the anterior flexion angle (AFA) and dropped head angle (DHA), which were validated with conventional manual labeling methods.Results The automatically measured DHA and AFA were in excellent agreement with manual labeling methods (intraclass correlation coefficient > 0.95) with mean bias equal to or less than 3 degrees.Conclusion The deep learning-based pose-estimation algorithm objectively measured postural abnormalities in PD patients.
ISSN:2005-940X
2093-4939
DOI:10.14802/jmd.21129