Early-onset and late-onset Parkinson's disease exhibit a different profile of gait and posture features based on the Kinect.

INTRODUCTION: Gait and posture abnormalities are the common disabling motor symptoms in Parkinson's disease (PD). This study aims to investigate the differential characteristics of gait and posture in early-onset PD (EOPD) and late-onset PD (LOPD) using the Kinect depth camera. METHODS: Eighty-eight participants, including two subgroups of 22 PD patients and two subgroups of 22 healthy controls (HC) matched for age, sex, and height, were enrolled. Gait and posture features were quantitatively assessed using a Kinect-based system. A two-way analysis of variance was used to compare the difference between different subgroups. RESULTS: EOPD had a significantly higher Gait score than LOPD (p = 0.031). Specifically, decreased swing phase (p = 0.034) was observed in the EOPD group. Although the Posture score was similar between the two groups, LOPD was characterized by an increased forward flexion angle of the trunk at the thorax (p = 0.042) and a decreased forward flexion angle of the head relative to the trunk (p = 0.009). Additionally, age-independent features were observed in both PD subgroups, and post hoc tests revealed that EOPD generally performed worse gait features. In comparison, LOPD was characterized by worse performance in posture features. CONCLUSIONS: EOPD and LOPD exhibit different profiles of gait and posture features. The phenotype-specific characteristics likely reflect the distinct neurodegenerative processes between them.

Kinect-based objective evaluation of bradykinesia in patients with Parkinson's disease.

Objective: To quantify bradykinesia in Parkinson's disease (PD) with a Kinect depth camera-based motion analysis system and to compare PD and healthy control (HC) subjects. Methods: Fifty PD patients and twenty-five HCs were recruited. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was used to evaluate the motor symptoms of PD. Kinematic features of five bradykinesia-related motor tasks were collected using Kinect depth camera. Then, kinematic features were correlated with the clinical scales and compared between groups. Results: Significant correlations were found between kinematic features and clinical scales (P < 0.05). Compared with HCs, PD patients exhibited a significant decrease in the frequency of finger tapping (P < 0.001), hand movement (P < 0.001), hand pronation-supination movements (P = 0.005), and leg agility (P = 0.003). Meanwhile, PD patients had a significant decrease in the speed of hand movements (P = 0.003) and toe tapping (P < 0.001) compared with HCs. Several kinematic features exhibited potential diagnostic value in distinguishing PD from HCs with area under the curve (AUC) ranging from 0.684-0.894 (P < 0.05). Furthermore, the combination of motor tasks exhibited the best diagnostic value with the highest AUC of 0.955 (95% CI = 0.913-0.997, P < 0.001). Conclusion: The Kinect-based motion analysis system can be applied to evaluate bradykinesia in PD. Kinematic features can be used to differentiate PD patients from HCs and combining kinematic features from different motor tasks can significantly improve the diagnostic value.