Examining Human Walking Characteristics Using Video-Based Motion Tracking
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2021-06-18
Language
en
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Abstract
Markerless gait recognition is a fast-evolving eld. In the study of human
movement, it can be a great asset and be preferred over marker-based meth-
ods, because markers themselves are obtrusive and may be inaccurate. The
fast development in this eld is marked by innovations such as DeepLab-
Cut, a markerless pose estimation method based on transfer learning with
deep neural networks that approaches human-level labeling accuracy with
minimal training data. In order to test the viability of DeepLabCut, several
gait parameters were identi ed from videos of walking participants to repro-
duce known di erences in these parameters between men and women. These
parameters are walking velocity, step frequency, and step length, in which
previous research has shown signi cant di erences between men and women,
namely that men walk with greater velocity, with greater step length, but
with a lower frequency. Using a well-known dataset consisting of people
walking perpendicular to the camera, it was found that men walk with
greater velocity and greater step length, but it was not found that women
walk with greater frequency. These results have various consequences to
the implementations of gait-identifying software, and the development of
systems such as DeepLabCut. In the improvement of our understanding of
human walking, this research could be expanded to investigate the effects of
these parameters on the energy consumption of walking participants.
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Faculteit der Sociale Wetenschappen