AUTHOR=Yin Wenchao , Gao Hong , Liang Beichen , Liu Ruichen , Liu Yue , Shen Chenxin , Niu Xiaohui , Wang Cui 

TITLE=Quantitative analysis of gait parameters in Parkinson’s disease and the clinical significance

JOURNAL=Frontiers in Neurology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1527020

DOI=10.3389/fneur.2025.1527020

ISSN=1664-2295

ABSTRACT=BackgroundGait disorder is one of the clinical manifestations of Parkinson’s disease (PD). Investigating the characteristics of gait disorder in patients with PD and the changes in gait before and after taking levodopa is crucial for the recognition, diagnosis and treatment of gait disorders in PD patients.MethodsIn this study, we measured the gait parameters of 20 patients with PD and 17 healthy controls and analyzed the changes of gait parameters of these patients before and after taking levodopa. We also used gait parameters as input features and MDS-UPDRS III score (which was further subdivided into tremor and non-tremor part score) as output labels to train machine learning regression models.ResultsWe found that except for cadence and stride time, most gait parameters of PD patients, including plantar dorsiflexion angle, plantar flexion angle, stride length, velocity were all smaller than those of the healthy controls. Moreover, the severity of gait disorders correlated with the severity of motor symptoms. After taking levodopa, the stride length, velocity and cadence were increased, but stride time was decreased. We also found that the trained machine learning model could explain and predict the MDS-UPDRS III score and non-tremor part score, and the non-tremor part score was better than the MDS-UPDRS III score.ConclusionOur gait assessment work can help clinicians recognize gait disorder in PD patients and predict the severity of clinical symptoms.