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Abstract

This study examined if occluded joint locations, obtained from 2D markerless motion capture (single camera view), produced 2D joint angles with reduced agreement compared to visible joints, and if 2D frontal plane joint angles were usable for practical applications. Fifteen healthy participants performed over-ground walking whilst recorded by fifteen marker-based cameras and two machine vision cameras (frontal and sagittal plane). Repeated measures Bland-Altman analysis illustrated that markerless standard deviation of bias and limits of agreement for the occluded-side hip and knee joint angles in the sagittal plane were double that of the camera-side (visible) hip and knee. Camera-side sagittal plane knee and hip angles were near or within marker-based error values previously observed. While frontal plane limits of agreement accounted for 35–46% of total range of motion at the hip and knee, Bland-Altman bias and limits of agreement (-4.6–1.6 ± 3.7–4.2˚) were actually similar to previously reported marker-based error values. This was not true for the ankle, where the limits of agreement (± 12˚) were still too high for practical applications. Our results add to previous literature, highlighting shortcomings of current pose estimation algorithms and labelled datasets. As such, this paper finishes by reviewing methods for creating anatomically accurate markerless training data using marker-based motion capture data.
Original languageEnglish
Article numbere0293917
Number of pages16
JournalPLoS ONE
Volume18
Issue number11
Early online date9 Nov 2023
DOIs
Publication statusPublished - 9 Nov 2023

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files. Supplementary File 4 encompasses time normalised (101 points) 2D joint centre locations for every trial (130 trials accross 14 participants), in both the sagittal and frontal plane, for the markerless system and the reprojected marker-based motion capture system. The JSON file format is universally implemented.

Acknowledgements

We would like to thank Andrew Chapman from the Maths Resource Centre (MASH) at the University of Bath for his valuable assistance in performing the repeated measures Bland-Altman analysis.

Funding

This research was funded by the EPSRC, through CAMERA, the RCUK Centre for the Analysis of Motion, Entertainment Research and Applications (EP/ M023281/1 and EP/T022523/1).

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