Analysis of Rehabilitation Data by Multi-Dimensional Principal Component Analysis Method Using the Statistical Software R

被引：0

作者：

Ishidata, Akio ^{[1
]}

Aibara, Ukyo ^{[2
]}

Murakami, Jun ^{[3
]}

Yamamoto, Naoki ^{[3
]}

Saito, Satoko ^{[4
]}

Izumi, Takeshi ^{[5
]}

Kano, Nozomi ^{[5
]}

机构：

[1] Kumamoto Natl Coll Technol, Fac Liberal Studies, Kumamoto, Japan

[2] Univ Ryukyus, Fac Informat Engn, Dept Engn, Nishihara, Okinawa 90301, Japan

[3] Kumamoto Natl Coll Technol, Dept Human Oriented Informat Syst Engn, Kumamoto, Japan

[4] Kumamoto Rehabil Hosp, Dept Rehabil, Kumamoto, Japan

[5] Kumamoto Rehabil Hosp, Hlth Informat Management Sect, Kumamoto, Japan

来源：

COMPUTING, CONTROL AND INDUSTRIAL ENGINEERING IV | 2013年 / 823卷

关键词：

Multi-dimensional principal component analysis; Rehabilitation data; Functional independence measure; Statistical software; R; HOSVD;

D O I：

10.4028/www.scientific.net/AMR.823.650

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The multi-dimensional principal component analysis (MPCA), which is an extension of the well-known principal component analysis (PCA), is proposed to reduce the dimension and to extract the feature of the multi-dimensional data. We have analyzed the rehabilitation data, which is known as the Functional Independence Measure (ELM), routinely measured from inpatients of a hospital by using MPCA. This time, we implemented the MPCA program by the statistical software R, and carried out the analysis of that data with changed configuration from the previous works in the environment of the R statistical system. From the results, the usefulness and the effectiveness of MPCA analysis in the R environment are confirmed.

引用

页码：650 / +

页数：2

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