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

Akio Ishida; Ukyo Aibara; Jun Murakami; Naoki Yamamoto; Satoko Saito; Takeshi Izumi; Nozomi Kano

doi:10.4028/www.scientific.net/AMR.823.650

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 823Analysis of Rehabilitation Data by...

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

Abstract:

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 (FIM), 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.

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Periodical:

Advanced Materials Research (Volume 823)

Pages:

650-656

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.823.650

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Online since:

October 2013

Authors:

Akio Ishida, Ukyo Aibara, Jun Murakami, Naoki Yamamoto, Satoko Saito, Takeshi Izumi, Nozomi Kano

Keywords:

Functional Independence Measure, HOSVD, Multi-Dimensional Principal Component Analysis, Rehabilitation Data, Statistical Software R

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