Gait pattern classification using compact features extracted from intrinsic mode functions

Ronny K. Ibrahim; Eliathamby Ambikairajah; Branko G. Celler; Nigel H. Lovell

doi:10.1109/IEMBS.2008.4650050

Gait pattern classification using compact features extracted from intrinsic mode functions

Ronny K. Ibrahim, Eliathamby Ambikairajah, Branko G. Celler, Nigel H. Lovell

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

11 Citations (Scopus)

Abstract

Recent research work indicates that gait patterns are both non-linear and non-stationary signals and they can be analyzed using empirical mode decomposition. This paper describes gait pattern classification using features that are obtained by performing discrete cosine transforms (DCT) on intrinsic mode functions of five different human gait patterns. The DCT provides a compact 8-dimensional feature vector for gait pattern classification. Fifty two subjects participated in the experiment. The classification was performed using a Gaussian mixture model and an overall accuracy of 90.2% was achieved.

Original language	English
Title of host publication	Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	3852-3855
Number of pages	4
ISBN (Print)	9781424418152
DOIs	https://doi.org/10.1109/IEMBS.2008.4650050
Publication status	Published - 2008
Event	30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada Duration: 20 Aug 2008 → 25 Aug 2008

Other

Other	30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
Country/Territory	Canada
City	Vancouver, BC
Period	20/08/08 → 25/08/08

Access to Document

10.1109/IEMBS.2008.4650050

Cite this

Ibrahim, R. K., Ambikairajah, E., Celler, B. G., & Lovell, N. H. (2008). Gait pattern classification using compact features extracted from intrinsic mode functions. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 (pp. 3852-3855). [4650050] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IEMBS.2008.4650050

Ibrahim, Ronny K. ; Ambikairajah, Eliathamby ; Celler, Branko G. ; Lovell, Nigel H. / Gait pattern classification using compact features extracted from intrinsic mode functions. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2008. pp. 3852-3855

@inproceedings{793affba87a646ff9e625cbb54871db7,

title = "Gait pattern classification using compact features extracted from intrinsic mode functions",

abstract = "Recent research work indicates that gait patterns are both non-linear and non-stationary signals and they can be analyzed using empirical mode decomposition. This paper describes gait pattern classification using features that are obtained by performing discrete cosine transforms (DCT) on intrinsic mode functions of five different human gait patterns. The DCT provides a compact 8-dimensional feature vector for gait pattern classification. Fifty two subjects participated in the experiment. The classification was performed using a Gaussian mixture model and an overall accuracy of 90.2% was achieved. ",

author = "Ibrahim, {Ronny K.} and Eliathamby Ambikairajah and Celler, {Branko G.} and Lovell, {Nigel H.}",

year = "2008",

doi = "10.1109/IEMBS.2008.4650050",

language = "English",

isbn = "9781424418152",

pages = "3852--3855",

booktitle = "Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 ; Conference date: 20-08-2008 Through 25-08-2008",

}

Ibrahim, RK, Ambikairajah, E, Celler, BG & Lovell, NH 2008, Gait pattern classification using compact features extracted from intrinsic mode functions. in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08., 4650050, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 3852-3855, 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08, Vancouver, BC, Canada, 20/08/08. https://doi.org/10.1109/IEMBS.2008.4650050

Gait pattern classification using compact features extracted from intrinsic mode functions. / Ibrahim, Ronny K.; Ambikairajah, Eliathamby; Celler, Branko G.; Lovell, Nigel H.

Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2008. p. 3852-3855 4650050.

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

TY - GEN

T1 - Gait pattern classification using compact features extracted from intrinsic mode functions

AU - Ibrahim, Ronny K.

AU - Ambikairajah, Eliathamby

AU - Celler, Branko G.

AU - Lovell, Nigel H.

PY - 2008

Y1 - 2008

N2 - Recent research work indicates that gait patterns are both non-linear and non-stationary signals and they can be analyzed using empirical mode decomposition. This paper describes gait pattern classification using features that are obtained by performing discrete cosine transforms (DCT) on intrinsic mode functions of five different human gait patterns. The DCT provides a compact 8-dimensional feature vector for gait pattern classification. Fifty two subjects participated in the experiment. The classification was performed using a Gaussian mixture model and an overall accuracy of 90.2% was achieved.

AB - Recent research work indicates that gait patterns are both non-linear and non-stationary signals and they can be analyzed using empirical mode decomposition. This paper describes gait pattern classification using features that are obtained by performing discrete cosine transforms (DCT) on intrinsic mode functions of five different human gait patterns. The DCT provides a compact 8-dimensional feature vector for gait pattern classification. Fifty two subjects participated in the experiment. The classification was performed using a Gaussian mixture model and an overall accuracy of 90.2% was achieved.

UR - http://www.scopus.com/inward/record.url?scp=61849132286&partnerID=8YFLogxK

U2 - 10.1109/IEMBS.2008.4650050

DO - 10.1109/IEMBS.2008.4650050

M3 - Conference proceeding contribution

C2 - 19163553

AN - SCOPUS:61849132286

SN - 9781424418152

SP - 3852

EP - 3855

BT - Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08

Y2 - 20 August 2008 through 25 August 2008

ER -

Ibrahim RK, Ambikairajah E, Celler BG, Lovell NH. Gait pattern classification using compact features extracted from intrinsic mode functions. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2008. p. 3852-3855. 4650050 https://doi.org/10.1109/IEMBS.2008.4650050

Gait pattern classification using compact features extracted from intrinsic mode functions

Abstract

Other

Access to Document

Other files and links

Fingerprint

Cite this