Document Type
Honors Thesis
Abstract
The pandemic has led to a huge increase in the number of people teleworking these days. The growing time people spend in front of the computer elevates the risk of them developing bad posture habits that in the long run can result in health issues. It therefore becomes highly important to develop methods to monitor and correct the posture of people now adjusting to this new lifestyle of teleworking. Although there are many posture correction apps on the Google play store, almost all of them involve using some kind of expensive tracker that is placed on the body and are not widely accessible. To fix this problem, multiple posture detection models were developed to identify bad posture. We further compared the performances of these models with each other to find the model that performs better than the others.
Publication Date
5-2022
Language
English
Faculty Mentor of Honors Project
Shawn Gieser
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Kapoor, Rithik, "Creating and Comparing Seated Posture Classification Models Using Machine Learning and Computer Vision" (2022). 2022 Spring Honors Capstone Projects. 23.
https://mavmatrix.uta.edu/honors_spring2022/23