Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

Robert M. Kanko, Elise K. Laende, Gerda Strutzenberger, Marcus Brown, W. Scott Selbie, Vincent DePaul, Stephen H. Scott, Kevin J. Deluzio

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

90 Citas (Scopus)

Resumen

Spatiotemporal parameters can characterize the gait patterns of individuals, allowing assessment of their health status and detection of clinically meaningful changes in their gait. Video-based markerless motion capture is a user-friendly, inexpensive, and widely applicable technology that could reduce the barriers to measuring spatiotemporal gait parameters in clinical and more diverse settings. Two studies were performed to determine whether gait parameters measured using markerless motion capture demonstrate concurrent validity with those measured using marker-based motion capture and a pressure-sensitive gait mat. For the first study, thirty healthy young adults performed treadmill gait at self-selected speeds while marker-based motion capture and synchronized video data were recorded simultaneously. For the second study, twenty-five healthy young adults performed over-ground gait at self-selected speeds while footfalls were recorded using a gait mat and synchronized video data were recorded simultaneously. Kinematic heel-strike and toe-off gait events were used to identify the same gait cycles between systems. Nine spatiotemporal gait parameters were measured by each system and directly compared between systems. Measurements were compared using Bland-Altman methods, mean differences, Pearson correlation coefficients, and intraclass correlation coefficients. The results indicate that markerless measurements of spatiotemporal gait parameters have good to excellent agreement with marker-based motion capture and gait mat systems, except for stance time and double limb support time relative to both systems and stride width relative to the gait mat. These findings indicate that markerless motion capture can adequately measure spatiotemporal gait parameters of healthy young adults during treadmill and over-ground gait.

Idioma originalEnglish
Páginas (desde-hasta)110414
Número de páginas1
PublicaciónJournal of Biomechanics
Volumen122
DOI
EstadoPublished - jun. 9 2021
Publicado de forma externa

Nota bibliográfica

Publisher Copyright:
Copyright © 2021 Elsevier Ltd. All rights reserved.

ASJC Scopus Subject Areas

  • Biophysics
  • Biomedical Engineering
  • Orthopedics and Sports Medicine
  • Rehabilitation

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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