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

TL;DR

This paper presents a non-intrusive, camera-based automated system for assessing children's gait to evaluate executive functions, utilizing deep learning and achieving over 76% accuracy.

Contribution

It introduces a novel vision-based gait analysis system for children, avoiding wearable sensors, and demonstrates its effectiveness with a custom dataset and transfer learning.

Findings

Achieved 76.61% classification accuracy.

Developed a dataset with 27 children performing gait tests.

Utilized transfer learning from NTU-RGB+D 120 dataset.

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 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.