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

Abstract

Background Freezing 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. Methods The 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. Results In 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. Conclusion We 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.

Detection and assessment of Parkinson's disease based on gait analysis: A survey

“…Meanwhile, the pre-built model of the human body is constructed maturely to fit the extracted related marker positions. Nowadays, Mocap systems have become the golden standard in clinical gait analysis owing to the high tracking accuracy and sampling frequency (Moore et al, 2007 ; Zhang et al, 2018 ; Park et al, 2021 ). However, such systems consisting of multiple pre-deployed cameras are expensive and cumbersome, limiting the applications to hospitals and labs.…”

“…For vision-based systems, Guayacán and Mart́ınez ( 2021 ) recently proposed a 3D CNN model that took the spatiotemporal saliency maps of RGB images as input, which achieved 94.1% accuracy (11 PD and 11 HC) under the LOSO validation. In Park et al ( 2021 ), 98.1% detection rate (77 PD and 34 HC) was achieved by using the high precision spatiotemporal and kinematic gait parameters collected by the Mocap system. It can be seen that marker-based systems can provide superior performance in PD detection due the high precision in human skeleton capture, while markerless systems can be deployed in free-living environments.…”

“…In previous works, different ML classifiers were used for achieving this task, as listed in the lower part of Table 5 . In specific, Park et al ( 2021 ) achieved 98% accuracy on a dataset with 31 freezers and 46 PD patients without FOG, where high precision 3D gait kinematics were captured by the Mocap system.…”

Estimation of Average and Maximum Daily-Life Mobility Performance Using the Timed Up-and-Go: Exploring the Added Value of an Instrumented Timed Up-and-Go

“…Therefore, this complexity comes with a higher demand on postural control. This has been demonstrated, particularly in patients with Parkinson’s disease [22, 23]. For this reason, it is very important to carefully analyze the turning phases in TUG tests.…”

Association between Severity of Freezing of Gait and Turning Characteristics in People with Parkinson’s Disease

“…Freezers have higher cadence, increased step time variability, and disordered bilateral coordination during turning, deficits that correlate with a greater number of FOG episodes [13,14]. Furthermore, Park et al [15] identified the association between turning characteristics and NFOGQ scores including clinical characteristics demonstrating that increased disease severity in freezers was associated with motor deficits such as stepping inhibition and loss of automaticity during repeated weight locomotion during turning. In addition, provoking FOG or gait instability may occur more frequently during the "end" stage, which is the fourth turning quadrant of the 180 • turning task [10,16].…”