Applications and limitations of current markerless motion capture methods for clinical gait biomechanics

PeerJ

article

Logan Wade¹  Laurie Needham¹  Polly McGuigan¹  James Bilzon¹ 

[1] Department for Health, University of Bath;Centre for Analysis of Motion, Entertainment Research and Applications, University of Bath;Centre for Sport Exercise and Osteoarthritis Research Versus Arthritis, University of Bath

关键词: Marker-based;    Deep learning;    Computer vision;    Pose estimation;    Clinical gait analysis;    OpenPose;    DeepLabCut;   

DOI  :  10.7717/peerj.12995

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundMarkerless motion capture has the potential to perform movement analysis with reduced data collection and processing time compared to marker-based methods. This technology is now starting to be applied for clinical and rehabilitation applications and therefore it is crucial that users of these systems understand both their potential and limitations. This literature review aims to provide a comprehensive overview of the current state of markerless motion capture for both single camera and multi-camera systems. Additionally, this review explores how practical applications of markerless technology are being used in clinical and rehabilitation settings, and examines the future challenges and directions markerless research must explore to facilitate full integration of this technology within clinical biomechanics.MethodologyA scoping review is needed to examine this emerging broad body of literature and determine where gaps in knowledge exist, this is key to developing motion capture methods that are cost effective and practically relevant to clinicians, coaches and researchers around the world. Literature searches were performed to examine studies that report accuracy of markerless motion capture methods, explore current practical applications of markerless motion capture methods in clinical biomechanics and identify gaps in our knowledge that are relevant to future developments in this area.ResultsMarkerless methods increase motion capture data versatility, enabling datasets to be re-analyzed using updated pose estimation algorithms and may even provide clinicians with the capability to collect data while patients are wearing normal clothing. While markerless temporospatial measures generally appear to be equivalent to marker-based motion capture, joint center locations and joint angles are not yet sufficiently accurate for clinical applications. Pose estimation algorithms are approaching similar error rates of marker-based motion capture, however, without comparison to a gold standard, such as bi-planar videoradiography, the true accuracy of markerless systems remains unknown.ConclusionsCurrent open-source pose estimation algorithms were never designed for biomechanical applications, therefore, datasets on which they have been trained are inconsistently and inaccurately labelled. Improvements to labelling of open-source training data, as well as assessment of markerless accuracy against gold standard methods will be vital next steps in the development of this technology.

【 授权许可】

CC BY