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Multivariate Analysis of Joint Motion Data by Kinect: Application to Parkinson's Disease.
IEEE Transactions on Neural Systems and Rehabilitation Engineering ( IF 4.8 ) Pub Date : 2019-11-15 , DOI: 10.1109/tnsre.2019.2953707
Peng Ren , Jorge F. Bosch Bayard , Li Dong , Jinying Chen , Lu Mao , Dan Ma , Mario Alvarez Sanchez , Daniel Mondejar Morejon , Maria L. Bringas , Dezhong Yao , Marjan Jahanshahi , Pedro A. Valdes-Sosa

Analysis of joint motion data (AJMD) by Kinect, such as velocity, has been widely used in many research fields, many of which focused on how one joint moves with another, namely bivariate AJMD. However, these studies might not accurately reflect the motor symptoms in patients. The human body can be divided into six widely accepted parts (head, trunk and four limbs), which are interrelated and interact with each other. Therefore, in this study we attempted to investigate how the major joints of one body part move with the ones in another body part, namely multivariate AJMD. For method illustration, the motion data of sit-to-stand-to-sit for healthy participants and people with Parkinson disease (PD) were employed. Four types of multivariate AJMD were investigated by eigenspace-maximal-information-canonical-correlation-analysis, which obtained maximal- information-eigen-coefficients (MIECes), the parameters for quantifying the correlation between two sets of joints located in two different body parts. The results show that healthy participants have significantly higher MIECes than the PD patients (p-value < 0.0001). Furthermore, the area under the receiver operating characteristic curve value for the classification between healthy participants and PD patients reaches up to 1.00. In conclusion, we demonstrated the possibility of using multivariate AJMD for motion feature extraction, which may be helpful for medical research and engineering.

中文翻译:

Kinect对关节运动数据的多元分析:在帕金森氏病中的应用。

Kinect对关节运动数据(AJMD)的分析,例如速度,已广泛用于许多研究领域,其中许多研究集中在一个关节如何与另一关节运动,即双变量AJMD。但是,这些研究可能无法准确反映患者的运动症状。人体可以分为六个相互关联并相互影响的广泛接受的部分(头部,躯干和四肢)。因此,在这项研究中,我们试图研究一个身体部位的主要关节与另一个身体部位的主要关节如何运动,即多元AJMD。为了说明方法,采用了健康参与者和帕金森氏病(PD)患者从坐到站坐的运动数据。通过特征空间-最大信息-规范相关分析研究了四种类型的多元AJMD,它获得了最大信息特征系数(MIECes),即用于量化位于两个不同身体部位的两组关节之间的相关性的参数。结果表明,健康参与者的MIEC明显高于PD患者(p值<0.0001)。此外,在健康参与者和PD患者之间进行分类的接收器工作特性曲线值下的面积达到1.00。总之,我们证明了使用多元AJMD进行运动特征提取的可能性,这可能对医学研究和工程设计有帮助。结果表明,健康参与者的MIEC明显高于PD患者(p值<0.0001)。此外,在健康参与者和PD患者之间进行分类的接收器工作特性曲线值下的面积达到1.00。总之,我们证明了使用多元AJMD进行运动特征提取的可能性,这可能对医学研究和工程设计有帮助。结果表明,健康参与者的MIEC明显高于PD患者(p值<0.0001)。此外,在健康参与者和PD患者之间进行分类的接收器工作特性曲线值下的面积达到1.00。总之,我们证明了使用多元AJMD进行运动特征提取的可能性,这可能对医学研究和工程设计有帮助。
更新日期:2019-11-01
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