Automatic Measurement of Postural Abnormalities With a Pose Estimation Algorithm in Parkinson’s Disease
- Author:
Jung Hwan SHIN
1
;
Kyung Ah WOO
;
Chan Young LEE
;
Seung Ho JEON
;
Han-Joon KIM
;
Beomseok JEON
Author Information
- Publication Type:12
- From:Journal of Movement Disorders 2022;15(2):140-145
- CountryRepublic of Korea
- Language:English
-
Abstract:
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.
