JACIII Vol.21 No.7 pp. 1240-1250
doi: 10.20965/jaciii.2017.p1240


Time-Series Data Analysis Using Sliding Window Based SVD for Motion Evaluation

Yinlai Jiang*1, Isao Hayashi*2, Shuoyu Wang*3, and Kenji Ishida*4

*1The University of Electro-Communications
Chofu, Tokyo 182-8585, Japan

*2Kansai University
Takatsuki, Osaka 569-1095, Japan

*3Kochi University of Technology
Kami, Kochi 782-8502, Japan

*4Kurihara Central Hospital
Kurihara, Miyagi 987-2205, Japan

December 30, 2016
August 31, 2017
November 20, 2017
singular value decomposition, time-series data analysis, sliding window, motion analysis, walking difficulty evaluation

A method based on singular value decomposition (SVD) is proposed for extracting features from motion time-series data observed with various sensing systems. Matrices consisting of the sliding window (SW) subsets of time-series data are decomposed, yielding singular vectors as the patterns of the motion, and the singular values as a scalar, by which the corresponding singular vectors describe the matrices.

The sliding window based singular value decomposition was applied to analyze acceleration during walking. Three levels of walking difficulty were simulated by restricting the right knee joint in the measurement. The accelerations of the middles of the shanks and the back of the waist were measured and normalized before the SW-SVD was performed.The results showed that the first singular values inferred from the acceleration data of the restricted side (the right shank) significantly related to the increase of the restriction among all the subjects while there were no common trends in the singular values of the left shank and the waist. The SW-SVD was suggested to be a reliable method to evaluate walking disability. Furthermore, a 2D visualization tool is proposed to provide intuitive information about walking difficulty which can be used in walking rehabilitation to monitor recovery.

Cite this article as:
Y. Jiang, I. Hayashi, S. Wang, and K. Ishida, “Time-Series Data Analysis Using Sliding Window Based SVD for Motion Evaluation,” J. Adv. Comput. Intell. Intell. Inform., Vol.21 No.7, pp. 1240-1250, 2017.
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