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Biomechanics

Development and Validation of Marker-based 2D Motion Analysis System for Clinical Gait Analysis

(E-161) Development and Validation of Marker-based 2D Motion Analysis System for Clinical Gait Analysis

Friday, October 13, 2023

3:30 PM – 4:30 PM PDT

Location: Exhibit Hall - Row E - Poster # 161

Presenting Author(s)

EH

Emma Hixenbaugh (she/her/hers)

Undergraduate Student
Florida Gulf Coast University
Lousville, Kentucky, United States

Primary Investigator(s)

RW

Ryan Wedge

Assistant Professor
East Carolina University / Department of Physical Therapy, United States

Introduction: Gait analysis is an important tool for assessing human movement and is commonly used in sports biomechanics and rehabilitation. However, this analysis often relies on observational assessments in the clinical setting or a 3D marker-based motion capture system which provides reliable data for continuous gait cycle analysis and is the current gold standard [2]. Typically, 3D marker-based motion capture systems are limited to the laboratory setting due to high cost and space requirements. As an alternative, 2D motion analysis software, such as Kinovea®, has been developed with the goal of providing a more accessible method for motion analysis. Previous clinical studies have explored these 2D motion analysis software options and developed systems to accurately collect and process gait data [2,3]. However, these systems have been limited to evaluating parameters at individual gait phases and utilizing one camera which inhibits the ability to compare parameters in different planes [2]. The goal of this project was to develop an accurate, integrated marker-based 2D motion analysis system comprised of three commercially available time-synced cameras, Noraxon® Ultium insole sensors, and Kinovea, a freely available software, for high resolution gait cycle data. The integration of these systems allows for a larger set of gait events to be analyzed across different planes of movement. The purpose of this analysis was to validate the developed system by comparing gait data from a single camera in the sagittal plane to a gold standard motion analysis system.

Materials and Methods: On a single participant, reflective markers as well as larger contrast markers were placed on the participant’s right leg and foot for motion capture. A GoPro® camera was used to record the participant’s gait from the right sagittal plane at 120 Hz and 1080p resolution. The Qualisys Motion System (200 Hz) was used simultaneously as the participant walked on a Bertec instrumented split-belt treadmill at 1.0 m/s and 1.5 m/s for three minutes at each speed. The vertical ground reaction force data (2000 Hz) from the instrumented treadmill and the Noraxon Ultium insole sensors (500 Hz) were used to gather stance time data. The GoPro videos were analyzed using Kinovea to obtain 2D marker position data for 75 strides of each limb for each speed. The validity of the motion analysis system was evaluated by comparing the results to Qualisys, the gold standard. Using a custom MATLAB program, the 2D marker position data from Kinovea and Qualisys were separately processed and then evaluated to calculate the difference in four different marker position as well as the following gait parameters: ankle angle, stride duration, and stance time. The motion data was normalized to 101 points for both the Qualisys and Kinovea data to find the difference in marker positions and ankle angles at each point of the stride cycle. Ranges and averages across the gait cycles are used to describe the differences. Differences between the two systems were compared using paired t-tests with a significance level of α = 0.05.

Results, Conclusions, and Discussions:

Results:

The average marker position difference across the strides was 10.35 ± 10.52 mm (p = 0.323-0.828) and 21.37 ± 19.35 mm (p = 0.432-0.772) in the horizontal direction and 5.57 ± 4.93 mm (p = < 0.001-0.710) and 7.31 ± 5.97 mm (p = 0.218-0.988) in the vertical direction at 1.0 m/s and 1.5 m/s, respectively. The average difference in ankle angles across all the strides was 3.3° ± 2.0° (p = 0.850) and 3.4° ± 2.5° (p = 0.918) at 1.0 m/s and 1.5 m/s, respectively. The average stride duration was 1.16 ± 0.02 seconds from Kinovea and 1.16 ± 0.03 seconds from Qualisys at 1.0 m/s (p = 0.630); the average stride duration was 1.00 ± 0.01 seconds from Kinovea and 1.01 ± 0.03 seconds from Qualisys at 1.5 m/s (p = 0.830). At 1.0 m/s, the average stance time on the left foot was 0.64 ± 0.05 seconds from Noraxon and 0.72 ± 0.02 seconds from Qualisys (p < 0.001), and the average stance time on the right was 0.63 ± 0.06 seconds from Noraxon and 0.72 ± 0.02 seconds from Qualisys (p < 0.001).

Discussion:

The difference in marker positions, ankle angles, and stride durations between the Kinovea and the Qualisys systems have an acceptable range of error with only a small deviation as speed increased [1,2]. The differences in stance times between the systems may be related to a lower sensitivity of the Ultium insoles compared to the force plates, which decreased the system’s ability to register when toe-off occurred.

Conclusion:

The results validated our developed motion analysis system which allows for the system to be used in a clinical setting with accuracy and reliability for continuous gait analysis of selected parameters. This system increases the accessibility of motion capture systems outside of the laboratory setting.

Acknowledgements (Optional):

This material is based upon work supported by the National Science Foundation under Grant No. 1950507. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

References (Optional) :

Eltoukhy, M., Oh, J., Kuenze, C., & Signorile, J. (2017). Improved kinect-based spatiotemporal and kinematic treadmill gait assessment. Gait & posture, 51, 77–83. https://doi.org/10.1016/j.gaitpost.2016.10.001

Fernández-González, P., Koutsou, A., Cuesta-Gómez, A., Carratalá-Tejada, M., Miangolarra-Page, J. C., & Molina-Rueda, F. (2020). Reliability of Kinovea® Software and Agreement with a Three-Dimensional Motion System for Gait Analysis in Healthy Subjects. Sensors (Basel, Switzerland), 20(11), 3154. https://doi.org/10.3390/s20113154

Wade, L., Needham, L., McGuigan, P., & Bilzon, J. (2022). Applications and limitations of current markerless motion capture methods for clinical gait biomechanics. PeerJ, 10, e12995. https://doi.org/10.7717/peerj.12995