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1. Develop and evaluate an augmented reality posture training tool to promote work safety

   https://doi.org/10.1177/1071181320641496  

   Chen, Ken; Perera, Gimantha; Li, Li; Xu, Xu; Chen, Karen B (December 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   null (Ed.)

   Conventional work posture training tools included pamphlets, one-time training orientation, and/or videos. These tools did not always yield satisfactory training outcomes, and the incident rate of work-related musculoskeletal disorders did not substantially lower. In this research, modern augmented reality (AR) technology was leveraged to deliver interactive, holistic, whole-body visual information to convey safe work postures. The developmental procedure followed DMAIC by first defining specifications of training content, which led to the development of the training tool, including 3D reconstruction of a virtual instructor and building of user interface based on user-centered framework. This AR training tool was measured and analyzed through usability evaluation, and quantitative and qualitative data were obtained for cross-validation and usability issue source identification. Findings revealed the utility of 3D reconstruction of a virtual instructor and practicality of adopting conventional usability evaluation method for AR user interface usability evaluation. Feedback from the usability evaluation via questionnaire, think aloud, and post-task open-ended responses are employed to iteratively design the next version of the AR posture training tool. 

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2. Impact of a VR/AR Module on First-Year Students’ Understanding of Architectural Engineering: A Comparison Across Demographics

   McArthur, M; Diefes-Dux, H; Erdogmus, E; Ryherd, E; Armwood-Gordon, C (August 2022, 2022 ASEE Annual Conference & Exposition)

   Architectural Engineering (AE) programs typically offer programs of study in at least two subdisciplines that relate to building design and construction. AE students may struggle to select a subdiscipline due to their low exposure and engagement with topics that would inform their decision. Previous research indicates that the immersive nature of virtual and augmented reality (VR/AR) can increase students’ engagement and motivation regarding course content and discipline decisions. The purpose of this study was to investigate the impact of a pilot browser-based virtual reality intervention on first-year AE students’ understanding of the AE subdisciplines and choice for future study, with particular attention to demographic subgroup differences. This pilot was a testbed for a larger set of Virtual/Augmented-Reality-Based Discipline Exploration Rotations (VADERs), which are instructional modules that engage students in authentic AE tasks. In Fall 2020, the pilot VADER-1 module was incorporated into first-year AE courses at one R1 university and one R2 historically black university. This module encouraged students to explore AE subdisciplines through the virtual design of a clinic. Pre-post intervention surveys were used to investigate change in students’ understanding of AE and its subdisciplines. Students reported that their subdiscipline understanding increased significantly. Further, 74% of students reported an increase in their confidence in their choice, and over 30% of students changed their top subdiscipline choice. Some slight differences between demographic subgroups were found. VR/AR interventions hold promise for providing AE students with needed exposure to the practice of the discipline and its subdisciplines. 

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3. Using Augmented Reality (AR) to Bring the Past to Life in Informal Science Learning

   Herrick, I.R.; Sinatra, G.; Kennedy, A.; Nye, B.; Swartout, W.; Lindsey, E. (January 2022, National Association for Research in Science Teaching (NARST) International Conference)

   Schwartz, R.; Roehrig, G.; Martin-Hansen, L.; Kemp, P.; Utano, J. (Ed.)

   A key mission for museums is to engage a large and diverse public audience in science learning (Macdonald, 1997). To that end, science museums attempt to use immersive technologies in entertaining, socially oriented, and innovative ways. An example is the use of augmented reality (AR) to overlay virtual objects onto the real-world (Azuma, Baillot, Behringer, Feiner, Julier, & MacIntyre, 2001).We used a Design Based Research (DBR) approach to develop and test four features of an AR experience to promote place-based science learning in an museum setting. While quantitative differences were not found among conditions in knowledge gained, significant learning gains were seen from pre to post, illustrating the potential for place-based informal science learning. Incorporating AR technology into museum exhibits can update them with 21st tools to support visitor engagement in the learning experience. This research contributes to understanding of usability and logistical issues for different AR designs for a public, outdoor informal settings. 

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4. EditAR: A Digital Twin Authoring Environment for Creation of AR/VR and Video Instructions from a Single Demonstration

   https://doi.org/10.1109/ISMAR55827.2022.00048  

   Chidambaram, Subramanian; Reddy, Sai Swarup; Rumple, Matthew; Ipsita, Ananya; Villanueva, Ana; Redick, Thomas; Stuerzlinger, Wolfgang; Ramani, Karthik (October 2022, IEEE International Symposium on Mixed and Augmented Reality (ISMAR))

   Augmented/Virtual reality and video-based media play a vital role in the digital learning revolution to train novices in spatial tasks. However, creating content for these different media requires expertise in several fields. We present EditAR, a unified authoring, and editing environment to create content for AR, VR, and video based on a single demonstration. EditAR captures the user’s interaction within an environment and creates a digital twin, enabling users without programming backgrounds to develop content. We conducted formative interviews with both subject and media experts to design the system. The prototype was developed and reviewed by experts. We also performed a user study comparing traditional video creation with 2D video creation from 3D recordings, via a 3D editor, which uses freehand interaction for in-headset editing. Users took 5 times less time to record instructions and preferred EditAR, along with giving significantly higher usability scores. 

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5. ARISE: High-Capacity AR Offloading Inference Serving via Proactive Scheduling

   https://doi.org/10.1145/3643832.3661894  

   Kong, Z Jonny; Xu, Qiang; Hu, Y Charlie (June 2024, ACM)

   With faster wireless networks and server GPUs, offloading high-accuracy but compute-intensive AR tasks implemented in Deep Neural Networks (DNNs) to edge servers offers a promising way to support high-QoE Augmented/Mixed Reality (AR/MR) applications. A cost-effective way for AR app vendors to deploy such edge-assisted AR apps to support a large user base is to use commercial Machine-Learning-as-a-Service (MLaaS) deployed at the edge cloud. To maximize cost-effectiveness, such an MLaaS provider faces a key design challenge, \ie how to maximize the number of clients concurrently served by each GPU server in its cluster while meeting per-client AR task accuracy SLAs. The above AR offloading inference serving problem differs from generic inference serving or video analytics serving in one fundamental way: due to the use of local tracking which reuses the last server-returned inference result to derive results for the current frame, the offloading frequency and end-to-end latency of each AR client directly affect its AR task accuracy (for all the frames). In this paper, we present ARISE, a framework that optimizes the edge server capacity in serving edge-assisted AR clients. Our design exploits the intricate interplay between per-client offloading schedule and batched inference on the server via proactively coordinating offloading request streams from different AR clients. Our evaluation using a large set of emulated AR clients and a 10-phone testbed shows that \name supports 1.7x--6.9x more clients compared to various baselines while keeping the per-client accuracy within the client-specified accuracy SLAs. 

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