Wu Zhuang, Gu Hongkai, Hong Ronghua, Xing Ziwen, Zhang Zhuoyu, Peng Kangwen, He Yijing, Xie Ludi, Zhang Jingxing, Gao Yichen, Jin Yue, Su Xiaoyun, Zhi Hongping, Guan Qiang, Pan Lizhen, Jin Lingjing
Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Persons' Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China.
Neurotoxin Research Center, Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Digit Health. 2023 May 19;9:20552076231176653. doi: 10.1177/20552076231176653. eCollection 2023 Jan-Dec.
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.
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.
Significant correlations were found between kinematic features and clinical scales ( < 0.05). Compared with HCs, PD patients exhibited a significant decrease in the frequency of finger tapping ( < 0.001), hand movement ( < 0.001), hand pronation-supination movements ( = 0.005), and leg agility ( = 0.003). Meanwhile, PD patients had a significant decrease in the speed of hand movements ( = 0.003) and toe tapping ( < 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 ( < 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, < 0.001).
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.
使用基于Kinect深度相机的运动分析系统对帕金森病（PD）中的运动迟缓进行量化，并比较PD患者与健康对照（HC）受试者。
招募了50名PD患者和25名HC受试者。使用运动障碍协会赞助修订的统一帕金森病评定量表第三部分（MDS-UPDRS III）评估PD的运动症状。使用Kinect深度相机收集五项与运动迟缓相关的运动任务的运动学特征。然后，将运动学特征与临床量表进行相关性分析，并在组间进行比较。
运动学特征与临床量表之间存在显著相关性（<0.05）。与HC受试者相比，PD患者的手指敲击频率（<0.001）、手部运动（<0.001）、手部旋前-旋后运动（=0.005）和腿部敏捷性（=0.003）显著降低。同时，与HC受试者相比，PD患者的手部运动速度（=0.003）和足趾敲击速度（<0.001）显著降低。几个运动学特征在区分PD与HC方面表现出潜在的诊断价值，曲线下面积（AUC）范围为0.684-0.894（<0.05）。此外，运动任务的组合表现出最佳诊断价值，最高AUC为0.955（95%CI=0.913-0.997，<0.001）。
基于Kinect的运动分析系统可用于评估PD中的运动迟缓。运动学特征可用于区分PD患者与HC受试者，并且结合不同运动任务的运动学特征可显著提高诊断价值。
