Disease Recognition and Classification from Movement Patterns

被引：0

作者：

Bhatia, Garima ^{[1
]}

Rani, Sangeeta ^{[1
]}

机构：

[1] Amity Univ, Dept Comp Sci & Engn, Noida, Uttar Pradesh, India

来源：

PROCEEDINGS OF THE 10TH INDIACOM - 2016 3RD INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT | 2016年

关键词：

K nearest neighbor (KNN); support vector machine (SVM); cluster validity and analysis platform (CVAP); Parkinson disease (PD); motion capture system; multi layer perceptron (MLP);

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, a motion capture system consisting of tags attached to the body is used to capture the movement pattern of the people. A classifier is trained so that it is used to classify the user's movement into various categories namely Normal, Parkinson disease, Hemiplegia, Leg pain and Back pain. The five different techniques are applied for the classification 1.) Decision tree classifier, 2.) K- Nearest Neighbor, 3.) Neural Networks Algorithm, 4.) Naive Bayes classifier and 5.) Support Vector Machine.

引用

页码：3682 / 3687

页数：6

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