<p>The recent development of Virtual Reality technology, researchers are looking more into changing the way Virtual Reality is used in our daily lives in order to increase our productivity. One such application is the mapping of 3D spatial graphics in Computer Aided Design engineering where practitioners have been historically working on 3D models in a two dimensional environment. Researchers in Computer Graphics have proposed Virtual Reality as a more effective medium for CAD packages. This thesis carries out a user study to test whether or not 3D VR environments are more effective in relaying information to the users as compared to two dimensional displays such as computer screens by conducting a study to determine how users navigate and interact with complex CAD objects in the two different environments. The two environments make use of stereoscopic vision and monoscopic vision in order to compare the efficiency with which volunteers are able to notice subtle differences in objects. The motivation for this study stems from the fact that CAD in VR is largely an underdeveloped topic and the result of such a study could form a baseline and advocate for further research and development in this domain. The research question being addressed is “Does CAD in a three-dimensional Virtual Reality Environment(stereoscopic) allow for better understanding of shapes of complex assemblies as compared to CAD on two-dimensional (monoscopic) computer screens?” The findings of this study suggest that rather than just the display technique the kind of movements which objects undergo also contributes to the way users perceive the objects in 3D vs 2D spaces and uncover a set of directions which would be recommended for similar studies in the future.</p><div><br></div>

<p>  </p> <p>Hazard identification is one of the most significant components in safety management at construction jobsites to prevent undesired fatalities and injuries of construction workers. The current practice, which relies on a limited number of safety managers’ manual and subjective inspections, and existing research efforts analyzing workers’ physical and physiological signals have achieved limited success, leaving many hazards unidentified at the jobsites. Motivated by this critical need, this research aims to develop a human sensing network that allows for ubiquitous hazard identification in the construction workplace.</p> <p>To attain this overarching goal, this research analyzes construction workers’ collective EEG signals collected from wearable EEG sensors based on machine learning, virtual reality (VR), and advanced signal processing techniques. Three specific research objectives are: (1) establishing a relationship between EEG signals and the existence of construction hazards, (2) identifying correlations between EEG signals/physiological states (e.g., emotion) and different hazard types, and (3) developing an integrated platform for real-time construction hazard mapping and comparing the results developed based on VR and real-world experimental settings.</p> <p>Specifically, the first objective establishes the relationship by investigating the feasibility of identifying construction hazards using a binary EEG classifier developed in VR, which can capture EEG signals associated with perceived hazards. In the second objective, correlations are discovered by testing the feasibility of differentiating construction hazard types based on a multi-class classifier constructed in VR. In the first and second objectives, the complex relationships are also analyzed in terms of brain dynamics and EEG signal components. In the third objective, the platform is developed by fusing EEG signals with heterogeneous data (e.g., location), and the discrepancies in VR and real-world environments are quantitatively assessed in terms of hazard identification performance and human behavioral responses.</p> <p>The primary outcome of this research is that the proposed approach can be applied to actual construction jobsites and used to detect all potential hazards, which was challenging to be achieved based on the current practice and existing research efforts. Also, the human cognitive mechanisms revealed in this research discover new neurocognitive knowledge in construction workers’ hazard perception. As a result, this research contributes to enhancing current hazard identification capability and improving construction workers’ safety and health.</p>