Abstract:
Motion analysis is one of the known ways used
to establish whether an infant is normal or abnormal. Studies
have indicated that there is a clear difference in terms of speed
or even time taken to respond to stimuli. General movements
(GMs) are spontaneous movements of infants that involves the
entire body differing in speed, amplitude and sequence. The
assessment of GMs has helped in identifying infants that are
at risk of neurological disorders. GMs assessment is based on
videos recorded by parents or caregivers which are then rated
by a clinicians or trained professionals. The General Assessment
Tool has worked well, however, it is time consuming and very
expensive. Several techniques have been proposed to automate
the General Movement assessment tool which include markerbased techniques and markerless techniques. In our review we
have systematically discussed the design features and technologies
involved in both of them and identified both the strength and
weakness. Thereafter, we explain the reasons for their limited
practical performance. We conclude by proposing a deep learning
approach that can be used to possibly address the issues raised
in the existing techniques.
