Multi-Channel Physiological-Based inference of Brain States for Urban Optimization
- Revanth Reddy, Ph.D. Student, Department of Biomedical Engineering, Computational Medicine Laboratory
MENTOR:
- Rose Faghih, Ph.D., Associate Professor, Department of Biomedical Engineering, Center for Urban Science + Progress, Computational Medicine Laboratory
Abstract
The Yerkes-Dodson law suggests that humans have an optimal arousal level that maximizes productivity(i.e. too much or too little arousal would result in decreased performance). In an urban environment, containing a high density of residents and workers, it is difficult to track and optimize performance. The capstone team will build a wristwatch-like sensor and mobile infrastructure that can measure skin conductance response in multiple parallel channels in order to estimate cognitive arousal. Multiple parallel channels of skin conductance reduces the noise and artifacting in current devices, allowing for estimation and modulation of arousal and performance of users in an urban environment.