Autonomous gait event detection with portable single-camera gait kinematics analysis system

Cheng Yang, Ukadike Ugbolue, Andrew Kerr, Vladimir Stankovic, Lina Stankovic, Bruce Carse, Konstantinos T. Kaliarntas, Philip J. Rowe

Research output: Contribution to journalArticle

Abstract

Laboratory-based nonwearable motion analysis systems have significantly advanced with robust objective measurement of the limb motion, resulting in quantified, standardized, and reliable outcome measures compared with traditional, semisubjective, observational gait analysis. However, the requirement for large laboratory space and operational expertise makes these systems impractical for gait analysis at local clinics and homes. In this paper, we focus on autonomous gait event detection with our bespoke, relatively inexpensive, and portable, single-camera gait kinematics analysis system. Our proposed system includes video acquisition with camera calibration, Kalman filter + Structural-Similarity-based marker tracking, autonomous knee angle calculation, video-frame-identification-based autonomous gait event detection, and result visualization. The only operational effort required is the marker-template selection for tracking initialization, aided by an easy-to-use graphic user interface. The knee angle validation on 10 stroke patients and 5 healthy volunteers against a gold standard optical motion analysis system indicates very good agreement. The autonomous gait event detection shows high detection rates for all gait events. Experimental results demonstrate that the proposed system can automatically measure the knee angle and detect gait events with good accuracy and thus offer an alternative, cost-effective, and convenient solution for clinical gait kinematics analysis.

Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalJournal of Sensors
Volume2016
DOIs
Publication statusPublished - Jan 2016

Fingerprint

gait
Gait analysis
systems analysis
Kinematics
kinematics
Cameras
cameras
Kalman filters
User interfaces
Visualization
Calibration
Costs
markers
space laboratories
Motion analysis
limbs
strokes
acquisition
templates
costs

Cite this

Yang, Cheng ; Ugbolue, Ukadike ; Kerr, Andrew ; Stankovic, Vladimir ; Stankovic, Lina ; Carse, Bruce ; Kaliarntas, Konstantinos T. ; Rowe, Philip J. . / Autonomous gait event detection with portable single-camera gait kinematics analysis system. In: Journal of Sensors. 2016 ; Vol. 2016. pp. 1-8.
@article{a20fdfb11ee54a389a9bdac4c2bbfaf0,
title = "Autonomous gait event detection with portable single-camera gait kinematics analysis system",
abstract = "Laboratory-based nonwearable motion analysis systems have significantly advanced with robust objective measurement of the limb motion, resulting in quantified, standardized, and reliable outcome measures compared with traditional, semisubjective, observational gait analysis. However, the requirement for large laboratory space and operational expertise makes these systems impractical for gait analysis at local clinics and homes. In this paper, we focus on autonomous gait event detection with our bespoke, relatively inexpensive, and portable, single-camera gait kinematics analysis system. Our proposed system includes video acquisition with camera calibration, Kalman filter + Structural-Similarity-based marker tracking, autonomous knee angle calculation, video-frame-identification-based autonomous gait event detection, and result visualization. The only operational effort required is the marker-template selection for tracking initialization, aided by an easy-to-use graphic user interface. The knee angle validation on 10 stroke patients and 5 healthy volunteers against a gold standard optical motion analysis system indicates very good agreement. The autonomous gait event detection shows high detection rates for all gait events. Experimental results demonstrate that the proposed system can automatically measure the knee angle and detect gait events with good accuracy and thus offer an alternative, cost-effective, and convenient solution for clinical gait kinematics analysis.",
author = "Cheng Yang and Ukadike Ugbolue and Andrew Kerr and Vladimir Stankovic and Lina Stankovic and Bruce Carse and Kaliarntas, {Konstantinos T.} and Rowe, {Philip J.}",
year = "2016",
month = "1",
doi = "10.1155/2016/5036857",
language = "English",
volume = "2016",
pages = "1--8",
journal = "Journal of Sensors",
issn = "1687-725X",
publisher = "Hindawi Limited",

}

Yang, C, Ugbolue, U, Kerr, A, Stankovic, V, Stankovic, L, Carse, B, Kaliarntas, KT & Rowe, PJ 2016, 'Autonomous gait event detection with portable single-camera gait kinematics analysis system' Journal of Sensors, vol. 2016, pp. 1-8. https://doi.org/10.1155/2016/5036857

Autonomous gait event detection with portable single-camera gait kinematics analysis system. / Yang, Cheng; Ugbolue, Ukadike; Kerr, Andrew; Stankovic, Vladimir; Stankovic, Lina; Carse, Bruce; Kaliarntas, Konstantinos T.; Rowe, Philip J. .

In: Journal of Sensors, Vol. 2016, 01.2016, p. 1-8.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Autonomous gait event detection with portable single-camera gait kinematics analysis system

AU - Yang, Cheng

AU - Ugbolue, Ukadike

AU - Kerr, Andrew

AU - Stankovic, Vladimir

AU - Stankovic, Lina

AU - Carse, Bruce

AU - Kaliarntas, Konstantinos T.

AU - Rowe, Philip J.

PY - 2016/1

Y1 - 2016/1

N2 - Laboratory-based nonwearable motion analysis systems have significantly advanced with robust objective measurement of the limb motion, resulting in quantified, standardized, and reliable outcome measures compared with traditional, semisubjective, observational gait analysis. However, the requirement for large laboratory space and operational expertise makes these systems impractical for gait analysis at local clinics and homes. In this paper, we focus on autonomous gait event detection with our bespoke, relatively inexpensive, and portable, single-camera gait kinematics analysis system. Our proposed system includes video acquisition with camera calibration, Kalman filter + Structural-Similarity-based marker tracking, autonomous knee angle calculation, video-frame-identification-based autonomous gait event detection, and result visualization. The only operational effort required is the marker-template selection for tracking initialization, aided by an easy-to-use graphic user interface. The knee angle validation on 10 stroke patients and 5 healthy volunteers against a gold standard optical motion analysis system indicates very good agreement. The autonomous gait event detection shows high detection rates for all gait events. Experimental results demonstrate that the proposed system can automatically measure the knee angle and detect gait events with good accuracy and thus offer an alternative, cost-effective, and convenient solution for clinical gait kinematics analysis.

AB - Laboratory-based nonwearable motion analysis systems have significantly advanced with robust objective measurement of the limb motion, resulting in quantified, standardized, and reliable outcome measures compared with traditional, semisubjective, observational gait analysis. However, the requirement for large laboratory space and operational expertise makes these systems impractical for gait analysis at local clinics and homes. In this paper, we focus on autonomous gait event detection with our bespoke, relatively inexpensive, and portable, single-camera gait kinematics analysis system. Our proposed system includes video acquisition with camera calibration, Kalman filter + Structural-Similarity-based marker tracking, autonomous knee angle calculation, video-frame-identification-based autonomous gait event detection, and result visualization. The only operational effort required is the marker-template selection for tracking initialization, aided by an easy-to-use graphic user interface. The knee angle validation on 10 stroke patients and 5 healthy volunteers against a gold standard optical motion analysis system indicates very good agreement. The autonomous gait event detection shows high detection rates for all gait events. Experimental results demonstrate that the proposed system can automatically measure the knee angle and detect gait events with good accuracy and thus offer an alternative, cost-effective, and convenient solution for clinical gait kinematics analysis.

U2 - 10.1155/2016/5036857

DO - 10.1155/2016/5036857

M3 - Article

VL - 2016

SP - 1

EP - 8

JO - Journal of Sensors

JF - Journal of Sensors

SN - 1687-725X

ER -