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Examination of 2D frontal and sagittal markerless motion capture: Implications for markerless applications. | LitMetric

Examination of 2D frontal and sagittal markerless motion capture: Implications for markerless applications.

PLoS One

Centre for the Analysis of Motion, Entertainment Research and Applications, University of Bath, Bath, United Kingdom.

Published: November 2023

AI Article Synopsis

  • The study assessed the accuracy of joint angle measurements from occluded locations using 2D markerless motion capture compared to visible joints during walking.
  • Results showed that the bias and limits of agreement for hip and knee angles in occluded locations were significantly higher than those for visible angles, indicating reduced accuracy.
  • While frontal plane angles for hip and knee joints were acceptable for practical use, ankle angle measurements were found to be too inaccurate, prompting a discussion on improving pose estimation algorithms through better training data.

Article 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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635560PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0293917PLOS

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