XR Lab moved in Digital Futures

As the director of the Extended Reality lab (XR-Lab), I am thrilled to report that our XR-Lab has moved into the new Digital Futures Building at UC.

Our Lab will continue to broaden the scope of collaborations, using our expertise in both academic and professional fields in Virtual Reality, Augmented Reality, and Mixed Reality. We look forward to the long-standing collaborative relationships with faculty at the UC Digital Futures, Criminology and Justice program at CECH, Civil Engineering program, and transportation program at CEAS, Live Well Collaborative, Council on Ageing, Cincinnati Insurance Company, and Cincinnati Children’s Hospital and Medical Center.

Please visit our lab after August 2022 to check out the exciting lab space and facilities at the new UC Digital Future Building.

Location:

Room: 200 at Smart Connected Wing, & 207 VR?AR Center, Digital Future Building

3044 Reading Road, Cincinnati, OH 45206

Request a tour

XR-Lab projects

Contact:

Ming Tang, tangmg@ucmail.uc.edu

Director of Extended Reality Lab, University of Cincinnati.

IRiS Ignite talk

Ming Tang presented the recent research project at the annual conference hosted by the Institute for Research in Sensing (IRiS), May 25th and 26th, 2022 at UC. This event re-imagines the traditional academic conference to forge novel connections and stimulate new interdisciplinary conversations on the broad topic of sensing, including work on perception, sensor technology development, and ethical innovations in sensing research. 

Project:  Use eye-tracking to measure the effectiveness of safety vests

Team: Ming Tang, John Ash, Adekunle Adebisi, Julian Wang, Jiaqi Ma. 

Funded by the Ohio Department of Transportation (ODOT)

Work zones are an essential component of any state transportation agency’s construction and maintenance operations. As such, agencies apply numerous practices to keep their workers safe during construction operations. The Ohio Department of Transportation (ODOT) recently invested in several more advanced items to improve worker safety (and traveler safety, by hopefully reducing the number of crashes overall). Specifically, ODOT invested in Type 2 and 3 safety vests, halo lights, and reflectors on the back of dump trucks. In 2020, a team of researchers from the University of Cincinnati (UC) worked with the Ohio Department of Transportation to assess the effectiveness of safety vests for day and night use.

The simulation-based evaluation used measurements to create realistic retroreflective vests, lights, and other safety equipment in virtual scenarios. These items were then placed in different virtual work zone environments, each of which had different work zone setup conditions, traffic control, vests worn by workers, time of day/ambient lighting, etc. Through an eye-tracking experiment measuring participants’ gaze on workers in different virtual work zone scenarios and a driving simulator experiment in which participants drove through virtual work zones and were asked follow-up questions on worker conspicuity, subjective and objective measures of worker visibility were obtained.

 

 

More information on this project can be found at  Access Vest or ODOT research database

NSF: Future of Work

Ming Tang worked as a co-investigator on the project funded by the NSF Grant. 

Future of Work: Understanding the interrelationships between humans and technology to improve the quality of work-life in smart buildings.

Grant: #SES-2026594 PI:  David W. Wendell. co-PIs: Harfmann, Anton; Fry, Michael; Rebola, Claudia; co-Is: Pravin Bhiwapurkar, Ann Black, Annulla Linders, Tamara Lorenz, Nabil Nassif, John Seibert, Ming Tang, Nicholas Williams, and Danny T.Y. Wu.  01-01-2021 -12-31-2021 National Science Foundation $149,720. Awarded Level: Federal 

 

The primary goal of this proposed planning project is to assemble a diverse, multidisciplinary team of experts dedicated to devising a robust methodology for the collection, analysis, and correlation of existing discipline-specific studies and data. This endeavor focuses on buildings and their occupants, aiming to unearth previously undiscovered interactions. Our research will specifically delve into the intricate interrelationships between four key areas: 1) the overall performance of buildings, 2) the indoor and outdoor environmental conditions, 3) the physical health of the occupants, and 4) their satisfaction with the work environment. This comprehensive approach is designed to provide a holistic understanding of the dynamic between buildings and the well-being of the individuals within them.

 

Prof. Anton Harfmann developed the sensor towers.

 

Ming Tang spearheaded the development of a Digital Twin model, an innovative project integrating multiple historical sensor data sets into a comprehensive, interactive 3D model. This model encompasses several vital features: the capture, analysis, and visualization of historical data; cloud-based data distribution; seamless integration with Building Information Models (BIM); and an intuitive Web User Experience (UX). Building elements are extracted as metadata from the BIM model and then overlaid in screen-based and Virtual Reality (VR) interfaces, offering a multi-dimensional data view. Further details are available at the Cloud-based Digital Twin project for a more in-depth exploration of this work.

 

See more details on the Digital Twin workflow.

 

paper @ CAADRIA Conference

Tian. J., Tang, M., Wang. J., The effect of path environment on pedestrian’s route selection: A case study of University of Cincinnati.27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). Sydney, Australia. April. 2022. 

The present study on the influence of the path environment on pedestrians’ route selection is mostly concentrated on the urban level while rarely discussed from the architectural level. Taking the University of Cincinnati (Ohio, US) as an example, this study aims to investigate whether the difference in the environmental settings of the route will affect pedestrians’ walking experiences and future route selection, with the ultimate goal of ascertaining the underlying relationship between the route environments and the user behavior in the process of route selection and implementation. This study selected three routes from the Langsam library to the CEAS library. The research methods included data analytics, questionnaires, and comparative analysis. Firstly, through surveys and an E4 wristband, psychological and physiological data were collected. Secondly, Analysis of Variance (ANOVA) was used to examine whether there was a significant difference in pedestrians’ walking experience among the three routes. Thirdly, through the analysis of questionnaires, the factors that play an important role in pedestrians’ route selection were determined. It can be concluded that the three routes with different environmental settings bring a different experience to participants. More specifically, the level of comfort and openness of the route significantly affects the route selection of pedestrians, while the degree of fatigue during walking does not. To sum up, for the transition space from outdoor to indoor, the factors affecting pedestrian route selection include the route’s degree of comfort and openness.

The paper is based on Jing Tain’s MS Thesis. Please check out the full thesis here.

Building Safety Analysis with Machine Learning

Grant received:

  1. Geospatial Imagery Analytics Research. Phase I. Sponsored research by the Cincinnati Insurance Companies. PI. Tang. Co-PI: Jiaqi Ma. $59,000. Period: 02.2020- 12.2021. Completed.
  2. Geospatial Imagery Analytics Research. Phase II. Sponsored research by the Cincinnati Insurance Companies. P.I. Tang. $79,980. Period: 10.2021- 08.2022. Grant: G402236. 2021. Ongoing.
  3. Geospatial Imagery Analytics Research. Phase III. Sponsored research by the Cincinnati Insurance Companies. PI. Tang. $15,709. Period: 6.2022- 06.2023.
  4. Geospatial Imagery Analytics Research. Phase-4. Sponsored research by the Cincinnati Insurance Companies. PI. Tang. $48,646. Period: 1.2023- 10.2023.
  5. Geospatial Imagery Analytics Research. Phase-5. Sponsored research by the Cincinnati Insurance Companies. PI. Tang. $72,350. Period: 10.2023- 11.2024.

The goal is to use A. I, Machine Learning, Deep Learning algorithm to understand the correlations between building safety to its typology and context. Please contact Professor Ming Tang if you are a UC student and interested in participating in the project.

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