Automatic recognition and analysis of hemiplegia gait.
10.7507/1001-5515.201806044
- Author:
Yean ZHU
1
,
2
;
Weiyi XU
3
;
Rui WANG
2
;
Yang TONG
2
;
Wei LU
4
;
Haolun WANG
5
Author Information
1. Human Factors Ergonomics Lab, East China Jiaotong University, Nanchang 330013, P.R.China
2. Virtual Reality and Interactive Techniques Institute, East China Jiaotong University, Nanchang 330013, P.R.China.
3. Human Factors Ergonomics Lab, East China Jiaotong University, Nanchang 330013, P.R.China.
4. Rehabilitation medicine department, Jiangxi Provincial Peoples Hospital, Nanchang 330006, P.R.China.
5. Human Factors Ergonomics Lab, East China Jiaotong University, Nanchang 330013, P.R.China.haolun123@163.com.
- Publication Type:Journal Article
- Keywords:
Bayesian classification;
Kinect;
automatic identification;
hemiplegia gait;
random forest
- MeSH:
Algorithms;
Bayes Theorem;
Gait;
Gait Analysis;
methods;
Gait Disorders, Neurologic;
diagnosis;
Hemiplegia;
complications;
Humans;
Walking
- From:
Journal of Biomedical Engineering
2019;36(2):306-314
- CountryChina
- Language:Chinese
-
Abstract:
In this paper, the research has been conducted by the Microsoft kinect for windows v2 for obtaining the walking trajectory data from hemiplegic patients, based on which we achieved automatic identification of the hemiplegic gait and sorted the significance of identified features. First of all, the experimental group and two control groups were set up in the study. The three groups of subjects respectively completed the prescribed standard movements according to the requirements. The walking track data of the subjects were obtained straightaway by Kinect, from which the gait identification features were extracted: the moving range of pace, stride and center of mass (up and down/left and right). Then, the bayesian classification algorithm was utilized to classify the sample set of these features so as to automatically recognize the hemiplegia gait. Finally, the random forest algorithm was used to identify the significance of each feature, providing references for the diagnose of disease by ranking the importance of each feature. This thesis states that the accuracy of classification approach based on bayesian algorithm reaches 96%; the sequence of significance based on the random forest algorithm is step speed, stride, left-right moving distance of the center of mass, and up-down moving distance of the center of mass. The combination of step speed and stride, and the combination of step speed and center of mass moving distance are important reference for analyzing and diagnosing of the hemiplegia gait. The results may provide creative mind and new references for the intelligent diagnosis of hemiplegia gait.
