Date of Award
5-2022
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Engineering and Sciences
First Advisor
Aldo Fabregas Ariza
Second Advisor
Rodrigo Mesa Arango
Third Advisor
Luis Daniel Otero
Fourth Advisor
Mary Ann Gaal
Abstract
Supported employment is a service provided by social enterprises to help persons with disabilities (PWD) to assimilate into their work environment and maintain their jobs for as long as possible. Social enterprises that provide such services are able to receive incentives from the government but they still rely on a limited funding. Augmented reality (AR) has been tested in various fields such as remote support and training employees, including PWDs. This thesis aims to evaluate the operational performance of augmented reality in supported employment using a model-based systems engineering approach paired with a simulation model. The simulation model evaluates three different scenarios including in-person, AR, and hybrid support with different parameters such as number of coaches, lifespan of AR device. The results from the simulation model shows that the implementation of AR in supported employment has the potential to enhance the efficiency of training while also reducing operational cost. It is not realistic to fully implement AR in supported employment because some people may not be able to use the devices depending on their personal conditions. The implementation of AR in a hybrid mode is cost-effective and has the potential to increase training efficiency or coverage as compared to hiring an additional coach.
Recommended Citation
Pan, Ruo Lun, "Model-Based Operational Performance Evaluation of Augmented Reality Applications in Supported Employment Training" (2022). Theses and Dissertations. 1341.
https://repository.fit.edu/etd/1341