Document Type
Article
Abstract
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 visionbased 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.
Publication Date
7-2-2021
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Zahed, Mohammad Zaki; Ashwin, Ramesh Babu; Jaiswal, Ashish; Kyrarini, Maria; Bell, Morris; and Makedon, Fillia, "Automated system to measure Tandem Gait to assess executive functions in children" (2021). Association of Computing Machinery Open Access Agreement Publications. 48.
https://mavmatrix.uta.edu/utalibraries_acmoapubs/48