{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/141230"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/141230","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"The Influence of AR Head-Mounted Displays on Spatial Perception and Worker Response in Construction Training","abstract":"Construction job sites present significant risks that extend beyond physical hazards to include psychophysiological factors that shape workers' perceptions, attention, and decision-making. The mobility and influence of Augmented Reality Head-Mounted Displays (AR-HMDs) directly interact with these factors by altering how workers perceive their environment, process information, and manage sensory input. While AR-HMDs offer new opportunities for immersive, adaptive training in real-world construction scenarios, they also introduce cognitive and sensory risks that have been insufficiently explored in construction research. Now that AR-HMDs are deployed on construction sites, it's crucial to understand not only their technical performance but also the human-centered impacts on safety, perception, and decision-making. Existing studies have largely overlooked the psychophysiological constraints that influence safety outcomes; yet, understanding these responses is essential for both immediate task performance and long-term learning and risk-taking behaviors. To address these gaps, we develop theoretical constructs and framework variables to evaluate and predict spatiotemporal psychophysiological responses associated with AR-HMD use in construction training, specifically asking: What are the risks associated with AR-HMDs' spatial influence on perception in construction environments? The framework is developed and tested through four objectives, progressing from a detailed scoping literature review and evaluation of AR-HMD impact on humans to psychophysiological prediction and median severity-of-impact classification using EEG location matrices. Objective 1 develops a conceptual model that classifies AR-HMD and Human-Computer Interaction (HCI) risks and standardizes the domain language for evaluating these technologies, highlighting underexplored cognitive, sensory, and physical human-factor risks. Additionally, objective 1 identifies and classifies spatial perception variables; develops a mental model of how workers perceive and spatially analyze immersive AR-HMD environments; examines embodiment, presence, and spatial presence; and finalizes the theoretical framework for empirical testing. Objective 2 tests the framework in a controlled environment using a full-scale passive and active haptic frame. A within-subjects design captures both psychological (survey-based) and physiological (EEG, heart rate) responses, which are analyzed using Power Spectral Density (PSD), Independent Component Analysis, and regression modeling to identify hemispheric differences and misalignments between perceived safety and actual psychophysiological responses. Objective 3 advances the framework into predictive modeling, using the same haptic-frame environment, deep learning models, including 2D CNN-LSTM sequence modeling and 3D CNN-LSTM architectures that are applied to predict temporal and cognitive state changes from 4D EEG input (frequency, amplitude, time, channels), extending the framework from measurement to prediction. Together, these three objectives show how conceptual modeling, spatial perception analysis, experimental validation, and predictive analytics can be systematically connected to evaluate AR-HMD situational risks. The outcomes reveal the extent of spatial and behavioral influences across key variables, supporting the development of likelihood and severity matrices for academia and industry, as outlined in objective 4.","abstract_html":"Construction job sites present significant risks that extend beyond physical hazards to include psychophysiological factors that shape workers' perceptions, attention, and decision-making. The mobility and influence of Augmented Reality Head-Mounted Displays (AR-HMDs) directly interact with these factors by altering how workers perceive their environment, process information, and manage sensory input. While AR-HMDs offer new opportunities for immersive, adaptive training in real-world construction scenarios, they also introduce cognitive and sensory risks that have been insufficiently explored in construction research. Now that AR-HMDs are deployed on construction sites, it's crucial to understand not only their technical performance but also the human-centered impacts on safety, perception, and decision-making. Existing studies have largely overlooked the psychophysiological constraints that influence safety outcomes; yet, understanding these responses is essential for both immediate task performance and long-term learning and risk-taking behaviors. To address these gaps, we develop theoretical constructs and framework variables to evaluate and predict spatiotemporal psychophysiological responses associated with AR-HMD use in construction training, specifically asking: What are the risks associated with AR-HMDs' spatial influence on perception in construction environments? The framework is developed and tested through four objectives, progressing from a detailed scoping literature review and evaluation of AR-HMD impact on humans to psychophysiological prediction and median severity-of-impact classification using EEG location matrices. Objective 1 develops a conceptual model that classifies AR-HMD and Human-Computer Interaction (HCI) risks and standardizes the domain language for evaluating these technologies, highlighting underexplored cognitive, sensory, and physical human-factor risks. Additionally, objective 1 identifies and classifies spatial perception variables; develops a mental model of how workers perceive and spatially analyze immersive AR-HMD environments; examines embodiment, presence, and spatial presence; and finalizes the theoretical framework for empirical testing. Objective 2 tests the framework in a controlled environment using a full-scale passive and active haptic frame. A within-subjects design captures both psychological (survey-based) and physiological (EEG, heart rate) responses, which are analyzed using Power Spectral Density (PSD), Independent Component Analysis, and regression modeling to identify hemispheric differences and misalignments between perceived safety and actual psychophysiological responses. Objective 3 advances the framework into predictive modeling, using the same haptic-frame environment, deep learning models, including 2D CNN-LSTM sequence modeling and 3D CNN-LSTM architectures that are applied to predict temporal and cognitive state changes from 4D EEG input (frequency, amplitude, time, channels), extending the framework from measurement to prediction. Together, these three objectives show how conceptual modeling, spatial perception analysis, experimental validation, and predictive analytics can be systematically connected to evaluate AR-HMD situational risks. The outcomes reveal the extent of spatial and behavioral influences across key variables, supporting the development of likelihood and severity matrices for academia and industry, as outlined in objective 4.","abstract_has_math":false,"creators":["Withers, Jeremy W."],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Environmental Design and Planning","degree_department":"Myers-Lawson School of Construction","school":null,"contributors":[],"advisors":[],"committee_chairs":["Turkaslan Bulbul, Tanyel"],"committee_members":["Gao, Xinghua","Akanmu, Abiola Abosede","Roofigari-Esfahan, Nazila"],"year":2026,"date_issued":"2026-02-10","date_published":"2026-02-10","updated_at":"2026-07-22T22:19:10Z","subjects":["AEC","Augmented Reality (AR)","Head Mounted Display (HMD)","Human Factors (HF)","Human-Computer Interaction (HCI)","Homeostasis","Risks","Deep Learning"],"languages":["en"],"rights":["Creative Commons Attribution 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45669"],"render_values":[{"text":"vt_gsexam:45669","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/141230","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Turkaslan Bulbul, Tanyel"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Gao, Xinghua","Akanmu, Abiola Abosede","Roofigari-Esfahan, Nazila"]},{"key":"dc:contributor.department","label":"Department","values":["Myers-Lawson School of Construction"]},{"key":"dc:creator","label":"Author","values":["Withers, Jeremy W."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-02-11T09:00:28Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-02-11T09:00:28Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-02-10"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Environmental Design and Planning"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["AEC","Augmented Reality (AR)","Head Mounted Display (HMD)","Human Factors (HF)","Human-Computer Interaction (HCI)","Homeostasis","Risks","Deep Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45669"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/141230"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Construction job sites present significant risks that extend beyond physical hazards to include psychophysiological factors that shape workers' perceptions, attention, and decision-making. The mobility and influence of Augmented Reality Head-Mounted Displays (AR-HMDs) directly interact with these factors by altering how workers perceive their environment, process information, and manage sensory input. While AR-HMDs offer new opportunities for immersive, adaptive training in real-world construction scenarios, they also introduce cognitive and sensory risks that have been insufficiently explored in construction research. Now that AR-HMDs are deployed on construction sites, it's crucial to understand not only their technical performance but also the human-centered impacts on safety, perception, and decision-making. Existing studies have largely overlooked the psychophysiological constraints that influence safety outcomes; yet, understanding these responses is essential for both immediate task performance and long-term learning and risk-taking behaviors. To address these gaps, we develop theoretical constructs and framework variables to evaluate and predict spatiotemporal psychophysiological responses associated with AR-HMD use in construction training, specifically asking: What are the risks associated with AR-HMDs' spatial influence on perception in construction environments? The framework is developed and tested through four objectives, progressing from a detailed scoping literature review and evaluation of AR-HMD impact on humans to psychophysiological prediction and median severity-of-impact classification using EEG location matrices. Objective 1 develops a conceptual model that classifies AR-HMD and Human-Computer Interaction (HCI) risks and standardizes the domain language for evaluating these technologies, highlighting underexplored cognitive, sensory, and physical human-factor risks. Additionally, objective 1 identifies and classifies spatial perception variables; develops a mental model of how workers perceive and spatially analyze immersive AR-HMD environments; examines embodiment, presence, and spatial presence; and finalizes the theoretical framework for empirical testing. Objective 2 tests the framework in a controlled environment using a full-scale passive and active haptic frame. A within-subjects design captures both psychological (survey-based) and physiological (EEG, heart rate) responses, which are analyzed using Power Spectral Density (PSD), Independent Component Analysis, and regression modeling to identify hemispheric differences and misalignments between perceived safety and actual psychophysiological responses. Objective 3 advances the framework into predictive modeling, using the same haptic-frame environment, deep learning models, including 2D CNN-LSTM sequence modeling and 3D CNN-LSTM architectures that are applied to predict temporal and cognitive state changes from 4D EEG input (frequency, amplitude, time, channels), extending the framework from measurement to prediction. Together, these three objectives show how conceptual modeling, spatial perception analysis, experimental validation, and predictive analytics can be systematically connected to evaluate AR-HMD situational risks. The outcomes reveal the extent of spatial and behavioral influences across key variables, supporting the development of likelihood and severity matrices for academia and industry, as outlined in objective 4."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Construction is one of the most hazardous industries, where risks are shaped not only by physical dangers but also by how workers perceive and respond to their environment. Augmented Reality (AR) Head-Mounted Displays (HMDs) are now being introduced on construction sites as mobile tools for training and task support. These devices provide immersive, hands-free instructions directly in the field, creating new opportunities for safer and more efficient learning. At the same time, they may misalign perception with reality, introducing new cognitive, sensory, and decision-making risks that remain insufficiently understood. This dissertation examines these effects by focusing on how the left and right hemispheres of the brain respond differently during AR-HMD use. The left hemisphere is often associated with analytical, sequential, and rule-based processing, while the right hemisphere supports spatial awareness, attention, and holistic integration. In immersive AR training, workers must draw on both left- and right-hemisphere processing while navigating 3D spaces. If AR-HMD design disproportionately loads one hemisphere, it may create safety risks: workers focused too narrowly on a task may overlook hazards. At the same time, those immersed in spatial imagery may misjudge sequences or measurements. To address these gaps, we develop theoretical constructs and framework variables to evaluate and predict spatiotemporal responses associated with AR-HMD use in construction training, specifically asking: What situational risks are associated with AR-HMDs' spatial influence on perception in construction environments? To investigate, this research integrates surveys with moment-to-moment recordings of brain activity and heart rate, capturing the contrast between what workers believe happened (subjective perception) and what their body reveals actually happened (physiological response). Guided by theories such as Risk Homeostasis and Human Factors, a framework is developed to explore under-examined issues, including spatial presence, situational awareness, and sensory misalignment. Finally, advanced 2D and 3D deep learning models are applied to classify and predict cognitive states with 67%-90% accuracy, demonstrating that worker responses can be measured and predicted. The results highlight both the promise and the risks of AR-HMDs in construction. On the one hand, immersive and haptic-based training can enhance engagement, skill development, and attentional focus; on the other hand, hemispheric cognitive differences can reveal vulnerabilities that, if overlooked, may compromise safety. By developing a holistic framework that integrates human perception, psychophysiological responses, and predictive modeling, this research lays the foundation for safer AR-HMD systems, more effective training methods, and enhanced protections for workers in an industry where risks remain high."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["The Influence of AR Head-Mounted Displays on Spatial Perception and Worker Response in Construction Training"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Turkaslan Bulbul, Tanyel"],"dc:contributor.committeemember":["Gao, Xinghua","Akanmu, Abiola Abosede","Roofigari-Esfahan, Nazila"],"dc:contributor.department":["Myers-Lawson School of Construction"],"dc:creator":["Withers, Jeremy W."],"dc:date.accessioned":["2026-02-11T09:00:28Z"],"dc:date.available":["2026-02-11T09:00:28Z"],"dc:date.issued":["2026-02-10"],"dc:description.abstract":["Construction job sites present significant risks that extend beyond physical hazards to include psychophysiological factors that shape workers' perceptions, attention, and decision-making. The mobility and influence of Augmented Reality Head-Mounted Displays (AR-HMDs) directly interact with these factors by altering how workers perceive their environment, process information, and manage sensory input. While AR-HMDs offer new opportunities for immersive, adaptive training in real-world construction scenarios, they also introduce cognitive and sensory risks that have been insufficiently explored in construction research. Now that AR-HMDs are deployed on construction sites, it's crucial to understand not only their technical performance but also the human-centered impacts on safety, perception, and decision-making. Existing studies have largely overlooked the psychophysiological constraints that influence safety outcomes; yet, understanding these responses is essential for both immediate task performance and long-term learning and risk-taking behaviors. To address these gaps, we develop theoretical constructs and framework variables to evaluate and predict spatiotemporal psychophysiological responses associated with AR-HMD use in construction training, specifically asking: What are the risks associated with AR-HMDs' spatial influence on perception in construction environments? The framework is developed and tested through four objectives, progressing from a detailed scoping literature review and evaluation of AR-HMD impact on humans to psychophysiological prediction and median severity-of-impact classification using EEG location matrices. Objective 1 develops a conceptual model that classifies AR-HMD and Human-Computer Interaction (HCI) risks and standardizes the domain language for evaluating these technologies, highlighting underexplored cognitive, sensory, and physical human-factor risks. Additionally, objective 1 identifies and classifies spatial perception variables; develops a mental model of how workers perceive and spatially analyze immersive AR-HMD environments; examines embodiment, presence, and spatial presence; and finalizes the theoretical framework for empirical testing. Objective 2 tests the framework in a controlled environment using a full-scale passive and active haptic frame. A within-subjects design captures both psychological (survey-based) and physiological (EEG, heart rate) responses, which are analyzed using Power Spectral Density (PSD), Independent Component Analysis, and regression modeling to identify hemispheric differences and misalignments between perceived safety and actual psychophysiological responses. Objective 3 advances the framework into predictive modeling, using the same haptic-frame environment, deep learning models, including 2D CNN-LSTM sequence modeling and 3D CNN-LSTM architectures that are applied to predict temporal and cognitive state changes from 4D EEG input (frequency, amplitude, time, channels), extending the framework from measurement to prediction. Together, these three objectives show how conceptual modeling, spatial perception analysis, experimental validation, and predictive analytics can be systematically connected to evaluate AR-HMD situational risks. The outcomes reveal the extent of spatial and behavioral influences across key variables, supporting the development of likelihood and severity matrices for academia and industry, as outlined in objective 4."],"dc:description.abstractgeneral":["Construction is one of the most hazardous industries, where risks are shaped not only by physical dangers but also by how workers perceive and respond to their environment. Augmented Reality (AR) Head-Mounted Displays (HMDs) are now being introduced on construction sites as mobile tools for training and task support. These devices provide immersive, hands-free instructions directly in the field, creating new opportunities for safer and more efficient learning. At the same time, they may misalign perception with reality, introducing new cognitive, sensory, and decision-making risks that remain insufficiently understood. This dissertation examines these effects by focusing on how the left and right hemispheres of the brain respond differently during AR-HMD use. The left hemisphere is often associated with analytical, sequential, and rule-based processing, while the right hemisphere supports spatial awareness, attention, and holistic integration. In immersive AR training, workers must draw on both left- and right-hemisphere processing while navigating 3D spaces. If AR-HMD design disproportionately loads one hemisphere, it may create safety risks: workers focused too narrowly on a task may overlook hazards. At the same time, those immersed in spatial imagery may misjudge sequences or measurements. To address these gaps, we develop theoretical constructs and framework variables to evaluate and predict spatiotemporal responses associated with AR-HMD use in construction training, specifically asking: What situational risks are associated with AR-HMDs' spatial influence on perception in construction environments? To investigate, this research integrates surveys with moment-to-moment recordings of brain activity and heart rate, capturing the contrast between what workers believe happened (subjective perception) and what their body reveals actually happened (physiological response). Guided by theories such as Risk Homeostasis and Human Factors, a framework is developed to explore under-examined issues, including spatial presence, situational awareness, and sensory misalignment. Finally, advanced 2D and 3D deep learning models are applied to classify and predict cognitive states with 67%-90% accuracy, demonstrating that worker responses can be measured and predicted. The results highlight both the promise and the risks of AR-HMDs in construction. On the one hand, immersive and haptic-based training can enhance engagement, skill development, and attentional focus; on the other hand, hemispheric cognitive differences can reveal vulnerabilities that, if overlooked, may compromise safety. By developing a holistic framework that integrates human perception, psychophysiological responses, and predictive modeling, this research lays the foundation for safer AR-HMD systems, more effective training methods, and enhanced protections for workers in an industry where risks remain high."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45669"],"dc:identifier.uri":["https://hdl.handle.net/10919/141230"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["Creative Commons Attribution 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by/4.0/"],"dc:subject":["AEC","Augmented Reality (AR)","Head Mounted Display (HMD)","Human Factors (HF)","Human-Computer Interaction (HCI)","Homeostasis","Risks","Deep Learning"],"dc:title":["The Influence of AR Head-Mounted Displays on Spatial Perception and Worker Response in Construction Training"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Environmental Design and Planning"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:10Z"}