Using Mobile Brain-Body Imaging to Develop an Evidence-based Cognitive Agent Framework (EBCAF) for Computational Spatial Navigation Simulation

The overarching goal of this research is to design technologies that improve the relationship between people and their created environment. The proposed research will develop and apply new methods for collecting rigorous data about human behaviors during navigational tasks in complex buildings. The empirical findings will be used to inform better computational simulations of human behavior in these environments, and help building designers to evaluate wayfinding features and obstacles in facility design prior to construction. This will ultimately improve the ability of building users to navigate successfully through the facilities, both in everyday tasks and in emergency situations, such as building evacuations. Building on prior work conducted in their lab, the research team will develop a mobile brain-body imaging system (MoBI), incorporating behavioral and physiological measurements, such as body-movement tracking, gaze tracking, and electroencephalography (EEG). This system will be applied in a study of human navigational behaviors during various wayfinding tasks in ⁓20 public buildings. The resulting data will significantly advance understanding of real-world navigation in built environments, and allow the PI to modernize the rather simplistic models of human behavior currently used in building-occupancy simulation software. The success of the new evidence-based, human-movement simulations in design practice will be evaluated under working conditions in coordination with three architectural design firms.

Roles and responsibilities 

Students will be involved in this large data collection during summer, in which we aim to recruit a total of 300 healthy adult participants. Each participant will be randomly assigned to complete navigational tasks in 2 real-world facilities, out of a total of 20 buildings used in the research. Students will be involved in training for data collection in this project, developing data collection protocol, curating data, and early analysis. Students will be able to work with a multidisciplinary team of researchers from computer science, design, and engineering.

Qualifications and previous coursework

This opportunity is available to non-graduating students in Cornell University's College of Human Ecology.

Previous exposure to empirical research, basic knowledge of statistical analysis (preferred). 

Learning outcomes 

• Students will learn:
• How to work with biometric data
• Data collection using Mobile Brain-body Imaging (MoBI) techniques
• Work in an interdisciplinary group of students with a diverse background
• Writing for publication