Measuring and understanding human motion is crucial in several domains,
ranging from neuroscience, to rehabilitation and sports biomechanics. Quantitative
information about human motion is fundamental to study how our
Central Nervous System controls and organizes movements to functionally
evaluate motor performance and deficits. In the last decades, the research in
this field has made considerable progress. State-of-the-art technologies that
provide useful and accurate quantitative measures rely on marker-based systems.
Unfortunately, markers are intrusive and their number and location must
be determined a priori. Also, marker-based systems require expensive laboratory
settings with several infrared cameras. This could modify the naturalness
of a subject\u2019s movements and induce discomfort. Last, but not less important,
they are computationally expensive in time and space. Recent advances on
markerless pose estimation based on computer vision and deep neural networks
are opening the possibility of adopting efficient video-based methods
for extracting movement information from RGB video data. In this contest,
this thesis presents original contributions to the following objectives: (i) the
implementation of a video-based markerless pipeline to quantitatively characterize
human motion; (ii) the assessment of its accuracy if compared with
a gold standard marker-based system; (iii) the application of the pipeline to
different domains in order to verify its versatility, with a special focus on the
characterization of the motion of preterm infants and on gait analysis. With
the proposed approach we highlight that, starting only from RGB videos and
leveraging computer vision and machine learning techniques, it is possible to
extract reliable information characterizing human motion comparable to that
obtained with gold standard marker-based systems