| Autor: |
Zadeh, Mohammad Zaki, Babu, Ashwin Ramesh, Jaiswal, Ashish, Kyrarini, Maria, Bell, Morris, Makedon, Fillia |
| Rok vydání: |
2020 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1145/3453892.3453999 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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