Automated system to measure Tandem Gait to assess executive functions in children
This work provides a less obtrusive method for assessing motor function in children, potentially useful for schools and homes, but the impact is incremental.
This paper developed a computer vision system to measure tandem gait in children using only a camera, avoiding obtrusive wearable sensors. The system achieved 76.61% classification accuracy in assessing children's performance.
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.