Differential and directional body coupling analysis in Parkinsonian gait
4 affiliations
- 1Department of Biomedical and Robotics Engineering, Incheon National University, Incheon, Republic of Korea
- 2Department of Industrial and Management Engineering, Incheon National University, Incheon, Republic of Korea
- 3Department of Neurology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
- 4Department of Neurology, Seoul Metropolitan Government–Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
Abstract
Parkinson’s disease (PD) is a progressive neurodegenerative disorder primarily characterized by motor impairments and altered whole-body coordination. Objective and accessible approaches for quantifying inter-limb and cross-directional coupling during gait remain limited in clinical settings. Using a prospectively collected dataset of individuals with PD and healthy controls (HC), we propose a comprehensive analytical framework based on human pose estimation to extract time-series kinematic signals from key body landmarks. Coupling strengths between body segments were quantified to characterize whole-body coordination patterns. Statistical filtering identified discriminative body coupling factors that significantly differentiated PD from HC. The identified factors demonstrated clear and consistent alterations in coordination patterns across groups. Furthermore, multiple machine learning classifiers validated their discriminative capacity, achieving robust classification performance. Correlation analyses between the top 10 pre-specified directional body coupling factors and MDS-UPDRS III scores demonstrated modest but statistically significant associations in the full PD cohort (n = 121), with 8 of 10 factors surviving FDR correction (ρ=0.27–0.31). These associations suggest that directional body coupling metrics capture clinically relevant variation in motor impairment severity beyond binary PD versus HC discrimination and may serve as potential digital biomarkers of PD-related motor dysfunction. Subtype-stratified analyses were conducted only as exploratory supplementary analyses, and larger, more balanced subtype cohorts are required before subtype-specific conclusions can be drawn. Overall, body coupling metrics provide reliable biomechanical markers of PD-related gait impairment and support the clinical potential of this non-invasive and cost-effective pipeline for early diagnosis and longitudinal monitoring.
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BibTeX
@article{zahir2026differential,
title = {{Differential and directional body coupling analysis in Parkinsonian gait}},
author = {Arifa Zahir and Jaehong Yu and Jin-Sun Jun and Kiwon Park and Ryul Kim and Hyundoo Jeong},
journal = {PLOS One},
year = {2026},
month = nov,
doi = {10.1371/journal.pone.0361959},
url = {https://doi.org/10.1371/journal.pone.0361959},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Differential and directional body coupling analysis in Parkinsonian gait
AU - Arifa Zahir
AU - Jaehong Yu
AU - Jin-Sun Jun
AU - Kiwon Park
AU - Ryul Kim
AU - Hyundoo Jeong
PY - 2026
DA - 2026/11/04
JO - PLOS One
DO - 10.1371/journal.pone.0361959
UR - https://doi.org/10.1371/journal.pone.0361959
AB - Parkinson’s disease (PD) is a progressive neurodegenerative disorder primarily characterized by motor impairments and altered whole-body coordination. Objective and accessible approaches for quantifying inter-limb and cross-directional coupling during gait remain limited in clinical settings. Using a prospectively collected dataset of individuals with PD and healthy controls (HC), we propose a comprehensive analytical framework based on human pose estimation to extract time-series kinematic signals from key body landmarks. Coupling strengths between body segments were quantified to characterize whole-body coordination patterns. Statistical filtering identified discriminative body coupling factors that significantly differentiated PD from HC. The identified factors demonstrated clear and consistent alterations in coordination patterns across groups. Furthermore, multiple machine learning classifiers validated their discriminative capacity, achieving robust classification performance. Correlation analyses between the top 10 pre-specified directional body coupling factors and MDS-UPDRS III scores demonstrated modest but statistically significant associations in the full PD cohort (n = 121), with 8 of 10 factors surviving FDR correction (ρ=0.27–0.31). These associations suggest that directional body coupling metrics capture clinically relevant variation in motor impairment severity beyond binary PD versus HC discrimination and may serve as potential digital biomarkers of PD-related motor dysfunction. Subtype-stratified analyses were conducted only as exploratory supplementary analyses, and larger, more balanced subtype cohorts are required before subtype-specific conclusions can be drawn. Overall, body coupling metrics provide reliable biomechanical markers of PD-related gait impairment and support the clinical potential of this non-invasive and cost-effective pipeline for early diagnosis and longitudinal monitoring.
SN - 1932-6203
ER -