Online learning of gait models for calculation of gait parameters

Jamie L.S. Waugh; Anton Trinh; Ryan R. Mohammed; William E. McIlroy; Dana Kulic

doi:10.1109/EMBC.2016.7592131

Online learning of gait models for calculation of gait parameters

Jamie L.S. Waugh, Anton Trinh, Ryan R. Mohammed, William E. McIlroy, Dana Kulic

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

2 Citations (Scopus)

Abstract

This paper proposes a novel approach for gait analysis from wearable sensing, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter and intrapersonal variability by creating an individualized model. Once the algorithm has converged to the input signal, key gait events can be identified relative to the estimated gait phase; these events can then be used to calculate gait parameters. The approach is implemented and tested on a human motion dataset where heel impact and toe takeoff events are extracted with an average error of 0.04 cycles.

Original language	English
Title of host publication	2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016)
Editors	Jose Principe, Jose Carmena, Justin Sanchez
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	6146-6149
Number of pages	4
ISBN (Electronic)	9781457702204
ISBN (Print)	9781457702198
DOIs	https://doi.org/10.1109/EMBC.2016.7592131
Publication status	Published - 2016
Externally published	Yes
Event	International Conference of the IEEE Engineering in Medicine and Biology Society 2016 - Walt Disney World Resort, Orlando, United States of America Duration: 16 Aug 2016 → 20 Aug 2016 Conference number: 38th https://embc.embs.org/2016/ https://ieeexplore.ieee.org/xpl/conhome/7580725/proceeding (Proceedings)

Conference

Conference	International Conference of the IEEE Engineering in Medicine and Biology Society 2016
Abbreviated title	EMBC 2016
Country/Territory	United States of America
City	Orlando
Period	16/08/16 → 20/08/16
Internet address	https://embc.embs.org/2016/ https://ieeexplore.ieee.org/xpl/conhome/7580725/proceeding (Proceedings)

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10.1109/EMBC.2016.7592131

Cite this

Waugh, J. L. S., Trinh, A., Mohammed, R. R., McIlroy, W. E., & Kulic, D. (2016). Online learning of gait models for calculation of gait parameters. In J. Principe, J. Carmena, & J. Sanchez (Eds.), 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) (pp. 6146-6149). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC.2016.7592131

Waugh, Jamie L.S. ; Trinh, Anton ; Mohammed, Ryan R. et al. / Online learning of gait models for calculation of gait parameters. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) . editor / Jose Principe ; Jose Carmena ; Justin Sanchez. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2016. pp. 6146-6149

@inproceedings{e3098af62ef94a37a5a314892d40a812,

title = "Online learning of gait models for calculation of gait parameters",

abstract = "This paper proposes a novel approach for gait analysis from wearable sensing, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter and intrapersonal variability by creating an individualized model. Once the algorithm has converged to the input signal, key gait events can be identified relative to the estimated gait phase; these events can then be used to calculate gait parameters. The approach is implemented and tested on a human motion dataset where heel impact and toe takeoff events are extracted with an average error of 0.04 cycles.",

author = "Waugh, {Jamie L.S.} and Anton Trinh and Mohammed, {Ryan R.} and McIlroy, {William E.} and Dana Kulic",

year = "2016",

doi = "10.1109/EMBC.2016.7592131",

language = "English",

isbn = "9781457702198",

pages = "6146--6149",

editor = "Jose Principe and Jose Carmena and Justin Sanchez",

booktitle = "2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016)",

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

address = "United States of America",

note = "International Conference of the IEEE Engineering in Medicine and Biology Society 2016, EMBC 2016 ; Conference date: 16-08-2016 Through 20-08-2016",

url = "https://embc.embs.org/2016/, https://ieeexplore.ieee.org/xpl/conhome/7580725/proceeding",

}

Waugh, JLS, Trinh, A, Mohammed, RR, McIlroy, WE & Kulic, D 2016, Online learning of gait models for calculation of gait parameters. in J Principe, J Carmena & J Sanchez (eds), 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) . IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 6146-6149, International Conference of the IEEE Engineering in Medicine and Biology Society 2016, Orlando, Florida, United States of America, 16/08/16. https://doi.org/10.1109/EMBC.2016.7592131

Online learning of gait models for calculation of gait parameters. / Waugh, Jamie L.S.; Trinh, Anton; Mohammed, Ryan R. et al.
2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) . ed. / Jose Principe; Jose Carmena; Justin Sanchez. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2016. p. 6146-6149.

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

TY - GEN

T1 - Online learning of gait models for calculation of gait parameters

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AU - McIlroy, William E.

AU - Kulic, Dana

N1 - Conference code: 38th

PY - 2016

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N2 - This paper proposes a novel approach for gait analysis from wearable sensing, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter and intrapersonal variability by creating an individualized model. Once the algorithm has converged to the input signal, key gait events can be identified relative to the estimated gait phase; these events can then be used to calculate gait parameters. The approach is implemented and tested on a human motion dataset where heel impact and toe takeoff events are extracted with an average error of 0.04 cycles.

AB - This paper proposes a novel approach for gait analysis from wearable sensing, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter and intrapersonal variability by creating an individualized model. Once the algorithm has converged to the input signal, key gait events can be identified relative to the estimated gait phase; these events can then be used to calculate gait parameters. The approach is implemented and tested on a human motion dataset where heel impact and toe takeoff events are extracted with an average error of 0.04 cycles.

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U2 - 10.1109/EMBC.2016.7592131

DO - 10.1109/EMBC.2016.7592131

M3 - Conference Paper

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BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016)

A2 - Principe, Jose

A2 - Carmena, Jose

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PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - International Conference of the IEEE Engineering in Medicine and Biology Society 2016

Y2 - 16 August 2016 through 20 August 2016

ER -

Waugh JLS, Trinh A, Mohammed RR, McIlroy WE, Kulic D. Online learning of gait models for calculation of gait parameters. In Principe J, Carmena J, Sanchez J, editors, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2016) . Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2016. p. 6146-6149 doi: 10.1109/EMBC.2016.7592131

Online learning of gait models for calculation of gait parameters

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