Removal of noise from the body-seat interface temperature signal using Empirical Mode Decomposition

Zhuofu Liu; Zhongming Luo; Andrew I Heusch; Vincenzo Cascioli; Peter W. McCarthy

Removal of noise from the body-seat interface temperature signal using Empirical Mode Decomposition

Zhuofu Liu^*, Zhongming Luo, Andrew I Heusch, Vincenzo Cascioli, Peter W. McCarthy

^*Corresponding author for this work

Faculty of Life Sciences and Education

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper applied the data-driven technique of Empirical Mode Decomposition (EMD) to smooth raw thermal data between body and seat interface. The performance of this EMD-based filter was compared with Moving Av erage (MA) filter, Local Regression (LR) filter and Savitzky-Golay (SG) filter using the goodness of statistics and error charts as judgment criteria. Results showed the EMD-based filter outperformed other de-noising algorithms with the lowest RMSE (root-mean-square-error) and the highest R ² values in the application of curve fitting based on the two power series.

Original language	English
Title of host publication	4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010
Pages	94-102
Number of pages	9
Publication status	Published - 2010
Event	4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010 - Harbin, China Duration: 2 Aug 2010 → 8 Aug 2010

Conference

Conference	4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010
Country/Territory	China
City	Harbin
Period	2/08/10 → 8/08/10

Keywords

Empirical Mode Decomposition
Noise removal
Seating
Temperature

Cite this

@inproceedings{101a7735ba7141d58d10fa8629e27093,

title = "Removal of noise from the body-seat interface temperature signal using Empirical Mode Decomposition",

abstract = "This paper applied the data-driven technique of Empirical Mode Decomposition (EMD) to smooth raw thermal data between body and seat interface. The performance of this EMD-based filter was compared with Moving Av erage (MA) filter, Local Regression (LR) filter and Savitzky-Golay (SG) filter using the goodness of statistics and error charts as judgment criteria. Results showed the EMD-based filter outperformed other de-noising algorithms with the lowest RMSE (root-mean-square-error) and the highest R 2 values in the application of curve fitting based on the two power series.",

keywords = "Empirical Mode Decomposition, Noise removal, Seating, Temperature",

author = "Zhuofu Liu and Zhongming Luo and Heusch, {Andrew I} and Vincenzo Cascioli and McCarthy, {Peter W.}",

year = "2010",

language = "English",

isbn = "9787121113154",

pages = "94--102",

booktitle = "4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010",

note = "4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010 ; Conference date: 02-08-2010 Through 08-08-2010",

}

Liu, Z, Luo, Z, Heusch, AI, Cascioli, V & McCarthy, PW 2010, Removal of noise from the body-seat interface temperature signal using Empirical Mode Decomposition. in 4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010. pp. 94-102, 4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010, Harbin, China, 2/08/10.

Removal of noise from the body-seat interface temperature signal using Empirical Mode Decomposition. / Liu, Zhuofu; Luo, Zhongming; Heusch, Andrew I et al.
4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010. 2010. p. 94-102.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Removal of noise from the body-seat interface temperature signal using Empirical Mode Decomposition

AU - Liu, Zhuofu

AU - Luo, Zhongming

AU - Heusch, Andrew I

AU - Cascioli, Vincenzo

AU - McCarthy, Peter W.

PY - 2010

Y1 - 2010

N2 - This paper applied the data-driven technique of Empirical Mode Decomposition (EMD) to smooth raw thermal data between body and seat interface. The performance of this EMD-based filter was compared with Moving Av erage (MA) filter, Local Regression (LR) filter and Savitzky-Golay (SG) filter using the goodness of statistics and error charts as judgment criteria. Results showed the EMD-based filter outperformed other de-noising algorithms with the lowest RMSE (root-mean-square-error) and the highest R 2 values in the application of curve fitting based on the two power series.

AB - This paper applied the data-driven technique of Empirical Mode Decomposition (EMD) to smooth raw thermal data between body and seat interface. The performance of this EMD-based filter was compared with Moving Av erage (MA) filter, Local Regression (LR) filter and Savitzky-Golay (SG) filter using the goodness of statistics and error charts as judgment criteria. Results showed the EMD-based filter outperformed other de-noising algorithms with the lowest RMSE (root-mean-square-error) and the highest R 2 values in the application of curve fitting based on the two power series.

KW - Empirical Mode Decomposition

KW - Noise removal

KW - Seating

KW - Temperature

M3 - Conference contribution

AN - SCOPUS:84862930628

SN - 9787121113154

SP - 94

EP - 102

BT - 4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010

T2 - 4th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2010

Y2 - 2 August 2010 through 8 August 2010

ER -

Removal of noise from the body-seat interface temperature signal using Empirical Mode Decomposition

Abstract

Conference

Keywords

Fingerprint

Cite this