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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.

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.

VR for Police Training

Active Shooter Simulation

Develop several fully immersive 3D VR active shooter scenarios that can run on cost-effective commercially available VR hardware.

Final Report for OCJS Project

Develop and Assess Active Shooter Virtual Reality Training for Ohio Law Enforcement.  PI: J.C Barnes. Co-PI: Tang Office of Criminal Justice Services. $50,000. 09. 2020-09.2021 ( $29,608)

Development of a Virtual Reality Augmented Violence Reduction Training System for Active and Mass Shooting incidents. PI: Ed Latessa. Co-PIs: J.C. Barnes, Ming Tang, Cory Haberman, Dan Gerard, Tim Sabransky. $10,000. Start-up fund. UC Digital Futures anchor tenant cohort.

Shimmer GSR sensor is used to test Physiological stress. 

Checklist

Using Checklists and Virtual Reality to Improve Police Investigations. Collaborative Research Advancement Grants. UC. $25,000. PI: Haberman. Co-PI: Tang, Barnes. Period: 07.2020-01.2022.

Team:

Ming Tang, Cory Haberman, J.C. Barnes, Cheryl Jonson, Dongrui Zhu, Heejin Lee, Jillian Desmond, Ruby Qiu, Snigdha Bhattiprolu, Rishyak Kommineni

Design Process

To create simulated human behavior, either during the active shooting, or the casual human dialogue, the team designed a A.I system to simulate the decision trees. Please watch the technique breakdown demo.