NSF: Future of Work

Ming Tang worked as 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 objective of this proposed planning project is to mobilize a multidisciplinary team of researchers to develop the methodology for collecting, analyzing, and correlating existing discipline-specific research and data about buildings and the workers in them in search of interactions that have not yet been uncovered. Specifically, we will explore the interrelationships among 1) overall building performance, 2) indoor and outdoor environmental conditions, 3) physical health, and 4) satisfaction with the work environment.

Ming Tang worked on the Digital Twin model to assemble multiple historical sensor data sets into an interactive 3D model.

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.

Grant under contract signning:

      4. Geospatial Imagery Analytics Research. Phase-4. Sponsored research by the Cincinnati Insurance Companies. PI. Tang. $48,646. Period: 1.2023- 10.2023.

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.

Login to the project resource page. ( password needed)

 

Virtual Reality for caregiver training

Assess the effectiveness of using Virtual Reality for caregiver training

Urban Health Pathway Seed Grant. PI: Ming Tang. Partner. Council on Ageing, LiveWell Collaborative. $19,844. 03. 2021-3.2022

Result: COA EVRTalk 

EVRTalk virtual reality caregiver training

 

This project aims to investigate the effectiveness of using Virtual Reality to build empathy for the care recipient by allowing the caregiver to experience day-to-day life from the care recipient’s perspective. Ming Tang leads a research team to work with COA and LiveWell Collaborative to develop and evaluate an expandable set of VR training modules designed to help train family and friends who are thrust into the caregiving role. Ming Tang lead the LWC team and design the simulated decision trees, scenarios, and hand-tracking technologies in an immersive VR environment.

COA is awarded $25,000 from the CTA Foundation Grant in 2021.

In the UC News. share point.

paper published in IJAEC

Ming Tang (2021). “Visual Perception: Eye-tracking and Real-time Walkthroughs in Architectural Design.” International Journal of Architecture, Engineering and Construction, 10(1), 1-9.

Visual Perception: Eye-tracking and Real-time Walkthroughs in Architectural Design

This paper discusses the application of Eye Tracking (ET) technologies as a new way for researchers to understand a person’s perception of a build environment regarding wayfinding and other spatial features. This method was beneficial for informing reviewers how an existing place or a proposed design was performing in terms of user experience. Combining ET with real-time walkthrough (RTW) and analytical platform allowed designers to make real-time changes and instantly see how these choices affected a user’s visual attention and interaction. This paper also presents a study investigating the architectural features emphasizing the simulated human behavioral cues and movement information as input parameters. The research is defined as a hybrid method that seeks augmented architectural experience, wayfinding and analyzes its’ performance using ET and RTW. While presenting their concepts through RTW, students used the Tobii Pro eye tracker and analytical software to investigate the attractiveness of the proposed experience related to the five spatial features: face, edge, intensity, blue-yellow contrast, and red-green contrast. The studio projects extended psychological architecture study by exploring, collecting, analyzing, and visualizing behavioral data and using the ET analysis to optimize the design presented through walking and driving simulations. ET allowed students in the transit hub design studio to investigate various design iterations about human perception to enhance spatial organization and navigation.

Authors: Ming Tang (University of Cincinnati).
Issue: Vol 10, No 1 (2021)
Pages: 1-9
Section: Research Paper
DOI: http://dx.doi.org/10.7492/IJAEC.2021.001

This research project was conducted in fall, 2018 at the Urban Mobility Studio, supported by the UC Forward program at the University of Cincinnati. The studio re-flection and proposals are provided by the graduate students: Alan Bossman, Shreya Jasrapuria, Grant Koniski, Jianna Lee, Josiah Ebert, Taylour Upton, Kevin Xu, Yin-ing Fang, Ganesh Raman, Nicole Szparagowski, and Niloufar Kioumarsi. The thesis research was conducted by Lorrin Kline.