Concurrent validity of artificial intelligence-based markerless motion capture for over-ground gait analysis:​ a study of spatiotemporal parameters

Ripic, Zachary; Signorile, Joseph F.; Kuenze, Christopher; Eltoukhy, Moataz

Affiliations

¹Department of Kinesiology and Sport Sciences, School of Education & Human Development, University of Miami, Coral Gables, FL 33143, USA

²Center on Aging, Miller School of Medicine, University of Miami, Coral Gables, FL 33146, USA

³Department of Kinesiology, School of Education, Michigan State University, East Lansing, MI 48824, USA

Abstract

Gait analysis is used in research and clinical environments; yet several limitations exist in current methodologies. Markerless systems, utilizing high-speed video and artificial intelligence, eliminate most limitations encountered in marker-, depth-, or inertial sensor-based systems; however, further development is needed to improve their utility and accessibility in practice. Spatiotemporal parameters from 22 young adults were estimated during over-ground gait. Nine parameters were calculated using events determined from force plate information combined with foot segment tracking and from motion of the foot relative to the sacrum using marker-based and markerless tracking. Two-way mixed effects, single measurement, absolute agreement and relative consistency interclass correlation coefficients, Bland-Altman bias and limits of agreement, and Lin’s concordance correlations were used to examine the validity of parameters from markerless tracking compared to parameters calculated from gait event methods using force plates and marker-based tracking. Gait speed, stride length, step length, cycle time, and step time from the markerless system all showed strong agreement with the force plate method. Other markerless-determined parameters were not as accurate. Differences in stride width are attributable to inconsistencies in foot segment definitions between models; while differences in stance time, swing time, and double limb support time were influenced by gait event methods. Mean differences in gait parameters were smaller than meaningful clinical differences in Parkinson’s disease patients and within ranges of reference values for elderly subjects. Further studies are needed to determine the validity across other patient groups, but results support the continued development of markerless systems for over-ground gait analysis.

Links

DOI: 10.1016/j.jbiomech.2022.111278

PubMed: 36063770

WoS: 000876956200001

History

Accepted: 2022-08-25

Online: 2022-08-30

Cited Works (2)

Year	Entry
2021	Kanko RM, Laende EK, Strutzenberger G, Brown M, Selbie WS, DePaul V, Scott SH, Deluzio KJ. Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system. J Biomech. June 9, 2021;122:110414.
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Cited By (4)

Year	Entry
2023	Ripic Z, Signorile JF, Best TM, Jacobs KA, Nienhuis M, Whitelaw C, Moenning C, Eltoukhy M. Validity of artificial intelligence-based markerless motion capture system for clinical gait analysis: spatiotemporal results in healthy adults and adults with Parkinson’s disease. J Biomech. June 2023;155:111645.
2023	Yeadon MR, Pain MTG. Fifty years of performance‐related sports biomechanics research. J Biomech. June 2023;155:111666.
2023	Ripic Z, Theodorakos I, Andersen MS, Signorile JF, Best TM, Jacobs KA, Eltoukhy M. Prediction of gait kinetics using Markerless-driven musculoskeletal modeling. J Biomech. August 2023;157:111712.
2023	Ripic Z, Nienhuis M, Signorile JF, Best TM, Jacobs KA, Eltoukhy M. A comparison of three-dimensional kinematics between markerless and marker-based motion capture in overground gait. J Biomech. October 2023;159:111793.