Augmented Reality (AR) head-mounted display (HMD) provides users with an immersive virtual experience in the real world. The portability of this technology affords various information display options for construction workers that are not possible otherwise. The information delivered via an interactive user interface provides an innovative method to display complex building instructions, which is more intuitive and accessible compared with traditional paper documentations. However, there are still challenges hindering the practical usage of this technology at the construction jobsite. As a technical restriction, current AR HMD products have a limited field of view (FOV) compared to the human vision range. It leads to an uncertainty of how the obstructed view of display will affect construction workers' perception of hazards in their surrounding area. Similarly, the information displayed to workers requires rigorous testing and evaluation to make sure that it does not lead to information overload. Therefore, it is essential to comprehensively evaluate the impacts of using AR HMD from both perspectives of task performance and cognitive performance. This dissertation aims to bridge the gap in understanding the cognitive impacts of using AR HMD in construction assembly tasks. Specifically, it focuses on answering the following two questions: (1) How are task performance and cognitive skills affected by AR displays under complex working conditions? (2) How are moment-to-moment changes of mental workload captured and evaluated during construction assembly tasks?
To answer these questions, this dissertation proposed two experiments. The first study tests two AR displays (conformal and tag-along) and paper instruction under complex working conditions, involving different framing scales and interference settings. Subjective responses are collected and analyzed to evaluate overall mental workload and situation awareness. The second study focuses on exploring an electroencephalogram (EEG) based approach for moment-to-moment capture and evaluation of mental workload. It uncovers the cognitive change on the time domain and provides room for further quantitative analyzing on mental workload. Especially, two frameworks of mental workload prediction are proposed by using (1) Long Short-Term Memory (LSTM) and (2) one-dimensional Convolutional Neural Network (1D CNN)-LSTM for forecasting EEG signal and, classifying task conditions and mental workload levels respectively. The approaches are tested to be effective and reliable for predicting and recognizing subjects' mental workload during assembly. In brief, this research contributes to the existing knowledge with an assessment of AR HMD use in construction assembly, including task performance evaluation and both subjective and physiological measurements for cognitive skills. / Doctor of Philosophy / Augmented Reality (AR) is an emerging technology that bridges the gap between virtual creatures and physical world with an immersive display experience. Today, head-mounted display (HMD) is well developed to meet the demands for portable AR devices. It provides interactive and intuitive display of 2D graphical information to make it easier to understand for users. Therefore, AR display has been studied in the past few years for a more simplified and productive construction assembly process. However, given the premise that construction is a high-risk industry, introducing such display technology to the jobsite needs to be carefully tested. One obstacle in current AR HMD products is the restriction of field of view (FOV), which may block users' view in presenting large-scale 3D objects. In construction assembly, workers need to deal with tasks in different scopes, such as wood framing for a residential house. Consequently, it is necessary to study how such technical challenge will impact workers' performance under different task conditions. Another concern comes from the mental perspective. Although AR display may bring convenience in acquiring effective information, it is difficult to measure if this generates excessive mental burden to users. Especially for construction workers, whether the overlaid display will cause distraction and information overload is crucial for protecting workers from hazards.
To address the problems, this dissertation explores the gap in previous literature, where mental workload is not well studied for using AR HMD in construction assembly. Two experiments are conducted to comprehensively evaluate the impacts of AR displays on both assembly performance and users' mental status. The outcomes bring implications to theoretical and practical aspects. First, it compares two AR displays (2D tag-along image and 3D conformal model) with traditional paper documentation for assembly performance (efficiency and accuracy) and users' cognitive skills (mental workload and situation awareness). The findings revealed the impact of FOV restriction and provided a strategic solution to selecting display method for different task conditions. Second, it proposes a physiological approach to calculate mental workload from analyzing the features from brain waves. It uncovered the latent mental changes during the assembly. Furthermore, two deep learning approaches are applied to predict and classify mental workload. The prediction model depicted the trend of mental workload in eighteen seconds based on an eighty-four-second training set, while the classifier recognized two task conditions with different mental workload levels with an accuracy of 93.6%. The results have promising potential for future research in detecting and preventing abnormality in workers' mental status. In addition, it is generalizable to apply in other construction tasks and AR applications.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115428 |
| Date | 14 June 2023 |
| Creators | Qin, Yimin |
| Contributors | Myers-Lawson School of Construction, Turkaslan Bulbul, Tanyel, Shealy, Earl Wade, Akanmu, Abiola Abosede, Gabbard, Joseph L. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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