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

Automated system to measure Tandem Gait to assess executive functions in children