Examining Human Walking Characteristics Using Video-Based Motion Tracking

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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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