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

Author:

Wu Zhuang12ORCID,Gu Hongkai12,Hong Ronghua12,Xing Ziwen12,Zhang Zhuoyu12,Peng Kangwen12,He Yijing12,Xie Ludi12,Zhang Jingxing2,Gao Yichen3,Jin Yue3,Su Xiaoyun3,Zhi Hongping3,Guan Qiang2,Pan Lizhen2,Jin Lingjing124

Affiliation:

1. 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

2. 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

3. IFLYTEK Suzhou Research Institute, Suzhou, China

4. Collaborative Innovation Center for Brain Science, Tongji University, Shanghai, China

Abstract

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.

Funder

the Clinical Technology Innovation Project of Shanghai Shenkang Hospital Development Center

the Sub-project of the Yangtze River Delta Regional Innovation Community Project of Shanghai Municipal Science and Technology Commission

the Science and Technology Innovation Action Plan of Shanghai Municipal Science and Technology Commission

the National Key Research and Development Program

Shanghai Blue Cross Brain Hospital Co., Ltd. and Shanghai Tongji University Education Development Foundation

Publisher

SAGE Publications

Subject

Health Information Management,Computer Science Applications,Health Informatics,Health Policy

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