Linear predictive modelling of gait patterns

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

doi:10.1109/ICASSP.2009.4959611

Linear predictive modelling of gait patterns

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

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

3 Citations (Scopus)

Abstract

The use of a wearable triaxial accelerometer for unsupervised monitoring of human movement has become a major research focus in recent years. In this paper, the relationship between accelerometry signals and human gait is analysed using a linear prediction (LP) model. We explore the use of the LP model for analysing five gait patterns and show that the LP cepstrum can be used for gait pattern classification with high accuracy. This is then compared to a filterbank based approach to estimate the cepstral coefficients. Fifty subjects participated in collection of gait pattern data involving walking on level surfaces, and walking up and down stairs and ramps. The results show that an overall accuracy of 93% can be achieved using features derived from the cepstral coefficients for the five different walking patterns.

Original language	English
Title of host publication	2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Subtitle of host publication	Proceedings
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	425-428
Number of pages	4
ISBN (Electronic)	9781424423545
ISBN (Print)	9781424423538
DOIs	https://doi.org/10.1109/ICASSP.2009.4959611
Publication status	Published - 2009
Externally published	Yes
Event	2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan Duration: 19 Apr 2009 → 24 Apr 2009

Other

Other	2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/Territory	Taiwan
City	Taipei
Period	19/04/09 → 24/04/09

Keywords

Gait classification
Gait modelling

Access to Document

10.1109/ICASSP.2009.4959611

Cite this

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title = "Linear predictive modelling of gait patterns",

abstract = "The use of a wearable triaxial accelerometer for unsupervised monitoring of human movement has become a major research focus in recent years. In this paper, the relationship between accelerometry signals and human gait is analysed using a linear prediction (LP) model. We explore the use of the LP model for analysing five gait patterns and show that the LP cepstrum can be used for gait pattern classification with high accuracy. This is then compared to a filterbank based approach to estimate the cepstral coefficients. Fifty subjects participated in collection of gait pattern data involving walking on level surfaces, and walking up and down stairs and ramps. The results show that an overall accuracy of 93% can be achieved using features derived from the cepstral coefficients for the five different walking patterns.",

keywords = "Gait classification, Gait modelling",

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

year = "2009",

doi = "10.1109/ICASSP.2009.4959611",

language = "English",

isbn = "9781424423538",

pages = "425--428",

booktitle = "2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009",

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

address = "United States",

note = "2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 ; Conference date: 19-04-2009 Through 24-04-2009",

}

Ibrahim, RK, Ambikairajah, E, Celler, BG & Lovell, NH 2009, Linear predictive modelling of gait patterns. in 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009: Proceedings., 4959611, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 425-428, 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, Taipei, Taiwan, 19/04/09. https://doi.org/10.1109/ICASSP.2009.4959611

Linear predictive modelling of gait patterns. / Ibrahim, Ronny K.; Ambikairajah, Eliathamby; Celler, Branko G. et al.
2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009: Proceedings. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2009. p. 425-428 4959611.

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

TY - GEN

T1 - Linear predictive modelling of gait patterns

AU - Ibrahim, Ronny K.

AU - Ambikairajah, Eliathamby

AU - Celler, Branko G.

AU - Lovell, Nigel H.

PY - 2009

Y1 - 2009

N2 - The use of a wearable triaxial accelerometer for unsupervised monitoring of human movement has become a major research focus in recent years. In this paper, the relationship between accelerometry signals and human gait is analysed using a linear prediction (LP) model. We explore the use of the LP model for analysing five gait patterns and show that the LP cepstrum can be used for gait pattern classification with high accuracy. This is then compared to a filterbank based approach to estimate the cepstral coefficients. Fifty subjects participated in collection of gait pattern data involving walking on level surfaces, and walking up and down stairs and ramps. The results show that an overall accuracy of 93% can be achieved using features derived from the cepstral coefficients for the five different walking patterns.

AB - The use of a wearable triaxial accelerometer for unsupervised monitoring of human movement has become a major research focus in recent years. In this paper, the relationship between accelerometry signals and human gait is analysed using a linear prediction (LP) model. We explore the use of the LP model for analysing five gait patterns and show that the LP cepstrum can be used for gait pattern classification with high accuracy. This is then compared to a filterbank based approach to estimate the cepstral coefficients. Fifty subjects participated in collection of gait pattern data involving walking on level surfaces, and walking up and down stairs and ramps. The results show that an overall accuracy of 93% can be achieved using features derived from the cepstral coefficients for the five different walking patterns.

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KW - Gait modelling

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DO - 10.1109/ICASSP.2009.4959611

M3 - Conference proceeding contribution

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SP - 425

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BT - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009

PB - Institute of Electrical and Electronics Engineers (IEEE)

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T2 - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009

Y2 - 19 April 2009 through 24 April 2009

ER -

Linear predictive modelling of gait patterns

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