  As mobile technologies have become ubiquitous in recent years, computer-based
cognitive tests have become more popular and efficient. In this work, we focus
on assessing motor function in children by analyzing their gait movements.
Although there has been a lot of research on designing automated assessment
systems for gait analysis, most of these efforts use obtrusive wearable sensors
for measuring body movements. We have devised a computer vision-based
assessment system that only requires a camera which makes it easier to employ
in school or home environments. A dataset has been created with 27 children
performing the test. Furthermore in order to improve the accuracy of the
system, a deep learning based model was pre-trained on NTU-RGB+D 120 dataset
and then it was fine-tuned on our gait dataset. The results highlight the
efficacy of proposed work for automating the assessment of children's
performances by achieving 76.61% classification accuracy.
