Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
Browse
3 results
Search Results
Item Restricted Industrial AI-based Workload Performance Analytics: Applications to Mixed Reality Multitasking(University of Illinois at Chicago, 2025) Abbas, Safanah Mohammed H; He, DavidImmersive technologies such as augmented and virtual reality are increasingly integrated into our daily lives. As this digital transformation progresses, understanding human reactions to these technologies becomes crucial, particularly in the context of human factors engineering, which prioritizes human safety and well-being. Mixed reality (MR), which blends the physical and virtual worlds, introduces new multitasking possibilities but also presents challenges. One critical aspect is the impact of MR multitasking on human workload, a key performance measure. This research employs an Industrial AI approach, combining traditional machine learning with advanced pre-trained models to develop predictive models for estimating human workload in MR environments. An experiment was conducted in which participants multitasked between a physical and a digital task within a defined timeframe. Workload data, collected via the NASA Task Load Index (NASA-TLX), was used alongside synthetic data generated by a Generative Adversarial Network (GAN) to create an enriched dataset. The combined real and synthetic data were then used to train predictive models, enhancing accuracy. To improve workload prediction, this study integrates pre-trained models such as BERT (Bidirectional Encoder Representations from Transformers) from large language models (LLMs) and CLIP (Contrastive Language-Image Pretraining) from computer vision applications, alongside traditional machine learning techniques like regression and neural networks. Evaluation using the Root Mean Square Error (RMSE) metric demonstrates that the proposed hybrid models incorporating transfer learning and pre-trained models significantly outperformed conventional methods. The deviations between actual and predicted values were minimal, indicating a more reliable workload estimation. This dissertation advances knowledge in human factors engineering by addressing a critical gap in workload prediction within MR multitasking environments. The findings provide insights into human-computer interaction in complex digital settings. Organizations implementing MR technologies can leverage these predictive models to better understand worker workload and optimize conditions for well-being and efficiency.16 0Item Restricted Industrial AI-based Workload Performance Analytics: Applications to Mixed Reality Multitasking(University of Illinois at Chicago, 2025) Abbas, Safanah; He, DavidImmersive technologies such as augmented and virtual reality are increasingly integrated into our daily lives. As this digital transformation progresses, understanding human reactions to these technologies becomes crucial, particularly in the context of human factors engineering, which prioritizes human safety and well-being. Mixed reality (MR), which blends the physical and virtual worlds, introduces new multitasking possibilities but also presents challenges. One critical aspect is the impact of MR multitasking on human workload, a key performance measure. This research employs an Industrial AI approach, combining traditional machine learning with advanced pre-trained models to develop predictive models for estimating human workload in MR environments. An experiment was conducted in which participants multitasked between a physical and a digital task within a defined timeframe. Workload data, collected via the NASA Task Load Index (NASA-TLX), was used alongside synthetic data generated by a Generative Adversarial Network (GAN) to create an enriched dataset. The combined real and synthetic data were then used to train predictive models, enhancing accuracy. To improve workload prediction, this study integrates pre-trained models such as BERT (Bidirectional Encoder Representations from Transformers) from large language models (LLMs) and CLIP (Contrastive Language-Image Pretraining) from computer vision applications, alongside traditional machine learning techniques like regression and neural networks. Evaluation using the Root Mean Square Error (RMSE) metric demonstrates that the proposed hybrid models incorporating transfer learning and pre-trained models significantly outperformed conventional methods. The deviations between actual and predicted values were minimal, indicating a more reliable workload estimation. This dissertation advances knowledge in human factors engineering by addressing a critical gap in workload prediction within MR multitasking environments. The findings provide insights into human-computer interaction in complex digital settings. Organizations implementing MR technologies can leverage these predictive models to better understand worker workload and optimize conditions for well-being and efficiency.8 0Item Restricted Enhancement of User Awareness of Virtual Content in Augmented Reality(Saudi Digital Library, 2023-03-02) Ghazwani, Yahya; Smith, Shamus; Blackmore, KarenAugmented reality (AR) technology combines the virtual and real world by superimposing virtual content onto the real-world environment. A key element that allows the user to view the virtual content combined with the real world is the AR display. While there is a wide variety of AR displays such as hand-held displays, projection-based displays, and head-mounted displays, the AR display field of view is limited compared to the typical human vision field of view. Consequently, the AR user can only perceive and interact with the virtual content through a limited view window. To address this problem, this research project has developed and evaluated the use of different AR notification alerts to enhance the AR user awareness of the virtual content out of their AR display's field of view. The alerts include on-screen AR notifications with both passive and active user interfaces, and 3D register notifications with both passive and active user interfaces. In a user study (n=24), the use of AR on-screen notifications with passive UI has been shown to alert and guide the AR user to virtual content out of their field view faster than the other three configurations. In addition, to validate the use of notifications to enhance the user's limited field of view, a study was conducted (n=46) to evaluate the use of notifications in a collaborative work environment with a limited field of view. The study compared the performance of participants in a collaborative work environment with and without the use of notifications. The study demonstrated that the participants that performed the collaborative tasks with the used notifications completed their task faster and made fewer mistakes than the control group that performed their collaborative task without the use of notifications.36 0