Classification of Parkinson’s disease with freezing of gait based on 360° turning analysis using 36 kinematic features

Journal of NeuroEngineering and Rehabilitation

Hwayoung Park¹  Changhong Youm²  Myeounggon Lee³  Byungjoo Noh⁴  Sungtae Shin⁵  Sang-Myung Cheon⁶ 

[1] Department of Health Sciences, The Graduate School of Dong-A University, Saha-gu, Busan, Republic of Korea;Department of Health Sciences, The Graduate School of Dong-A University, Saha-gu, Busan, Republic of Korea;Department of Healthcare and Science, College of Health Sciences, Dong-A University, 37 Nakdong‑Daero, 550 Beon‑gil, Hadan 2-dong, Saha-gu, 49315, Busan, Republic of Korea;Department of Health and Human Performance, Center for Neuromotor and Biomechanics Research, University of Houston, Houston, TX, USA;Department of Kinesiology, Jeju National University, Jeju-si, Jeju-do, Republic of Korea;Department of Mechanical Engineering, College of Engineering, Dong-A University, Saha-gu, Busan, Republic of Korea;Department of Neurology, School of Medicine, Dong-A University, 26, Daesingongwon-ro, Seo-gu, 49201, Busan, Republic of Korea;

关键词: Parkinson’s disease;    Machine learning;    Turning;    Falls;    Kinematics;   

DOI  :  10.1186/s12984-021-00975-4

来源: Springer

【 摘 要 】

BackgroundFreezing of gait (FOG) is a sensitive problem, which is caused by motor control deficits and requires greater attention during postural transitions such as turning in people with Parkinson’s disease (PD). However, the turning characteristics have not yet been extensively investigated to distinguish between people with PD with and without FOG (freezers and non-freezers) based on full-body kinematic analysis during the turning task. The objectives of this study were to identify the machine learning model that best classifies people with PD and freezers and reveal the associations between clinical characteristics and turning features based on feature selection through stepwise regression.MethodsThe study recruited 77 people with PD (31 freezers and 46 non-freezers) and 34 age-matched older adults. The 360° turning task was performed at the preferred speed for the inner step of the more affected limb. All experiments on the people with PD were performed in the “Off” state of medication. The full-body kinematic features during the turning task were extracted using the three-dimensional motion capture system. These features were selected via stepwise regression.ResultsIn feature selection through stepwise regression, five and six features were identified to distinguish between people with PD and controls and between freezers and non-freezers (PD and FOG classification problem), respectively. The machine learning model accuracies revealed that the random forest (RF) model had 98.1% accuracy when using all turning features and 98.0% accuracy when using the five features selected for PD classification. In addition, RF and logistic regression showed accuracies of 79.4% when using all turning features and 72.9% when using the six selected features for FOG classification.ConclusionWe suggest that our study leads to understanding of the turning characteristics of people with PD and freezers during the 360° turning task for the inner step of the more affected limb and may help improve the objective classification and clinical assessment by disease progression using turning features.

【 授权许可】

CC BY