MemoryScan: Smart Digital Transformation of Large-scale Environments for Eliciting Location Specific Knowledge
MemoryScan is a prototype community-scale virtual environment platform to elicit memory recall from current and former citizens and visitors of a selected municipality. Gathering recollections on a continuous community scale enables location-based stories to be visually tied together rather than exi...
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| Veröffentlicht in: | IEEE International Symposium on Mixed and Augmented Reality Workshops (Online) S. 207 - 211 |
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| Format: | Tagungsbericht |
| Sprache: | Englisch |
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IEEE
01.10.2022
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| ISSN: | 2771-1110 |
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| Abstract | MemoryScan is a prototype community-scale virtual environment platform to elicit memory recall from current and former citizens and visitors of a selected municipality. Gathering recollections on a continuous community scale enables location-based stories to be visually tied together rather than existing as locationally independent reflections. The interdisciplinary project team will examine the potential advantages of using a spatially realistic virtual environment to assist in the capture of participant reflections and collect supporting privately held documents. Designed as a large 3D photorealistic environment, MemoryScan will use an eXtended reality (XR) Head-Mounted Display (HMD) or PC interface to permit the capture of oral reflections of a community's heritage with remote participant access. Should this project's experimental testing validate this view, the fully developed system will be made available to crowdsource information on a national scale. This system would benefit historians, anthropologists, political scientists, city planners, and others. |
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| AbstractList | MemoryScan is a prototype community-scale virtual environment platform to elicit memory recall from current and former citizens and visitors of a selected municipality. Gathering recollections on a continuous community scale enables location-based stories to be visually tied together rather than existing as locationally independent reflections. The interdisciplinary project team will examine the potential advantages of using a spatially realistic virtual environment to assist in the capture of participant reflections and collect supporting privately held documents. Designed as a large 3D photorealistic environment, MemoryScan will use an eXtended reality (XR) Head-Mounted Display (HMD) or PC interface to permit the capture of oral reflections of a community's heritage with remote participant access. Should this project's experimental testing validate this view, the fully developed system will be made available to crowdsource information on a national scale. This system would benefit historians, anthropologists, political scientists, city planners, and others. |
| Author | Michlowitz, Robert A. Walters, Lori C. Kider, Joseph T. |
| Author_xml | – sequence: 1 givenname: Robert A. surname: Michlowitz fullname: Michlowitz, Robert A. email: robert.michlowitz@ucf.edu organization: University of Central Florida,IST, School of Modeling, Simulation, and Training – sequence: 2 givenname: Joseph T. surname: Kider fullname: Kider, Joseph T. email: joseph.kider@ucf.edu organization: University of Central Florida,IST, School of Modeling, Simulation, and Training – sequence: 3 givenname: Lori C. surname: Walters fullname: Walters, Lori C. email: lori.walters@ucf.edu organization: University of Central Florida,IST, School of Modeling, Simulation, and Training |
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| Snippet | MemoryScan is a prototype community-scale virtual environment platform to elicit memory recall from current and former citizens and visitors of a selected... |
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| SubjectTerms | Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Virtual Reality Human-centered computing-Interaction design-Systems and tools for interaction design Point cloud compression Prototypes Resists Solid modeling Three-dimensional displays Urban areas Virtual environments |
| Title | MemoryScan: Smart Digital Transformation of Large-scale Environments for Eliciting Location Specific Knowledge |
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