A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics

Ningcun Xu; Xiwei Peng; Liang Peng; Zeng Guang Hou

doi:10.1109/CCDC52312.2021.9601607

A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics

Ningcun Xu, Xiwei Peng, Liang Peng, Zeng Guang Hou

School of Automation

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

2 Citations (Scopus)

Abstract

In order to make up for the shortcomings of a single inertial sensor, which is easily disturbed and unable to directly describe the periodic characteristics of gait, a novel gait events detection algorithm is proposed which is based on the invariant characteristics of hip joint kinematics. Four healthy volunteers conducted a walking experiment over the ground, who were equipped with motion capture device and insole with foot-switch. The hip angle derived from motion capture device were applied to detect gait events, heel stride (HS) and toe off (TO). And the gait events detected by ground reactor force (GRF) were taken as the reference standard. The mean absolute difference of HS is 28 ± 42ms, and the mean absolute difference of TO is 27 ± 43ms. And the confidence levels of the two gait events are 97.5% and 99.2%, respectively. The results demonstrate that the proposed gait events detection algorithm is reliability and has potential clinical application value.

Original language	English
Title of host publication	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	861-866
Number of pages	6
ISBN (Electronic)	9781665440899
DOIs	https://doi.org/10.1109/CCDC52312.2021.9601607
Publication status	Published - 2021
Event	33rd Chinese Control and Decision Conference, CCDC 2021 - Kunming, China Duration: 22 May 2021 → 24 May 2021

Publication series

Name	Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

Conference

Conference	33rd Chinese Control and Decision Conference, CCDC 2021
Country/Territory	China
City	Kunming
Period	22/05/21 → 24/05/21

Keywords

gait event
hip joint kinematics
inertial sensor
statistical analysis

Access to Document

10.1109/CCDC52312.2021.9601607

Cite this

Xu, N., Peng, X., Peng, L., & Hou, Z. G. (2021). A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021 (pp. 861-866). (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC52312.2021.9601607

Xu, Ningcun ; Peng, Xiwei ; Peng, Liang et al. / A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics. Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 861-866 (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021).

@inproceedings{3a121c983f664d5ba390c8e2f1bd3f48,

title = "A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics",

abstract = "In order to make up for the shortcomings of a single inertial sensor, which is easily disturbed and unable to directly describe the periodic characteristics of gait, a novel gait events detection algorithm is proposed which is based on the invariant characteristics of hip joint kinematics. Four healthy volunteers conducted a walking experiment over the ground, who were equipped with motion capture device and insole with foot-switch. The hip angle derived from motion capture device were applied to detect gait events, heel stride (HS) and toe off (TO). And the gait events detected by ground reactor force (GRF) were taken as the reference standard. The mean absolute difference of HS is 28 ± 42ms, and the mean absolute difference of TO is 27 ± 43ms. And the confidence levels of the two gait events are 97.5% and 99.2%, respectively. The results demonstrate that the proposed gait events detection algorithm is reliability and has potential clinical application value.",

keywords = "gait event, hip joint kinematics, inertial sensor, statistical analysis",

author = "Ningcun Xu and Xiwei Peng and Liang Peng and Hou, {Zeng Guang}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 33rd Chinese Control and Decision Conference, CCDC 2021 ; Conference date: 22-05-2021 Through 24-05-2021",

year = "2021",

doi = "10.1109/CCDC52312.2021.9601607",

language = "English",

series = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "861--866",

booktitle = "Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021",

address = "United States",

}

Xu, N, Peng, X, Peng, L & Hou, ZG 2021, A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics. in Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 861-866, 33rd Chinese Control and Decision Conference, CCDC 2021, Kunming, China, 22/05/21. https://doi.org/10.1109/CCDC52312.2021.9601607

A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics. / Xu, Ningcun; Peng, Xiwei; Peng, Liang et al.
Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 861-866 (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021).

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

TY - GEN

T1 - A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics

AU - Xu, Ningcun

AU - Peng, Xiwei

AU - Peng, Liang

AU - Hou, Zeng Guang

PY - 2021

Y1 - 2021

N2 - In order to make up for the shortcomings of a single inertial sensor, which is easily disturbed and unable to directly describe the periodic characteristics of gait, a novel gait events detection algorithm is proposed which is based on the invariant characteristics of hip joint kinematics. Four healthy volunteers conducted a walking experiment over the ground, who were equipped with motion capture device and insole with foot-switch. The hip angle derived from motion capture device were applied to detect gait events, heel stride (HS) and toe off (TO). And the gait events detected by ground reactor force (GRF) were taken as the reference standard. The mean absolute difference of HS is 28 ± 42ms, and the mean absolute difference of TO is 27 ± 43ms. And the confidence levels of the two gait events are 97.5% and 99.2%, respectively. The results demonstrate that the proposed gait events detection algorithm is reliability and has potential clinical application value.

AB - In order to make up for the shortcomings of a single inertial sensor, which is easily disturbed and unable to directly describe the periodic characteristics of gait, a novel gait events detection algorithm is proposed which is based on the invariant characteristics of hip joint kinematics. Four healthy volunteers conducted a walking experiment over the ground, who were equipped with motion capture device and insole with foot-switch. The hip angle derived from motion capture device were applied to detect gait events, heel stride (HS) and toe off (TO). And the gait events detected by ground reactor force (GRF) were taken as the reference standard. The mean absolute difference of HS is 28 ± 42ms, and the mean absolute difference of TO is 27 ± 43ms. And the confidence levels of the two gait events are 97.5% and 99.2%, respectively. The results demonstrate that the proposed gait events detection algorithm is reliability and has potential clinical application value.

KW - gait event

KW - hip joint kinematics

KW - inertial sensor

KW - statistical analysis

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U2 - 10.1109/CCDC52312.2021.9601607

DO - 10.1109/CCDC52312.2021.9601607

M3 - Conference contribution

AN - SCOPUS:85125178098

T3 - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

SP - 861

EP - 866

BT - Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 33rd Chinese Control and Decision Conference, CCDC 2021

Y2 - 22 May 2021 through 24 May 2021

ER -

Xu N, Peng X, Peng L, Hou ZG. A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics. In Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 861-866. (Proceedings of the 33rd Chinese Control and Decision Conference, CCDC 2021). doi: 10.1109/CCDC52312.2021.9601607

A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics

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