Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study

Matteo Moro, Giorgia Marchesi, Filip Hesse, Francesca Odone, Maura Casadio

Abstract:

The analysis of human gait is an important tool in medicine and rehabilitation to evaluate
the effects and the progression of neurological diseases resulting in neuromotor disorders. In these
fields, the gold standard techniques adopted to perform gait analysis rely on motion capture systems
and markers. However, these systems present drawbacks: they are expensive, time consuming and
they can affect the naturalness of the motion. For these reasons, in the last few years, considerable
effort has been spent to study and implement markerless systems based on videography for gait
analysis. Unfortunately, only few studies quantitatively compare the differences between markerless
and marker-based systems in 3D settings. This work presented a new RGB video-based markerless
system leveraging computer vision and deep learning to perform 3D gait analysis. These results
were compared with those obtained by a marker-based motion capture system. To this end, we
acquired simultaneously with the two systems a multimodal dataset of 16 people repeatedly walking
in an indoor environment. With the two methods we obtained similar spatio-temporal parameters.
The joint angles were comparable, except for a slight underestimation of the maximum flexion for
ankle and knee angles. Taking together these results highlighted the possibility to adopt markerless
technique for gait analysis.