Interdisciplinary Collaboration Grants Program
The Division of Research established the Interdisciplinary Collaboration Grants (ICG) Program to provide funds to facilitate the development of collaborations at Binghamton University. This program is for investigators who seek to enhance their research opportunities through collaboration and may include projects that represent a new research agenda. Proposals from all areas of scholarship are encouraged.
Two projects received funding in the program's 2013-14 round of awards:
Zhanpeng Jin (Electrical and Computer Engineering), Sarah Laszlo (Psychology), and Kenneth Kurtz (Psychology)
“Brain Password: Exploring A Psychophysiological Approach for Secure User Authentication”
The massive breach of credit card data at Target in late 2013 raises severe concerns from the public and governments about potential security and privacy challenges in a cyber world. Conventional cryptographic credentials, however, are vulnerable to eavesdropping and can resist neither user's intentional disclosure nor coercion attacks. Even biometric keys can be surreptitiously duplicated, revealed, or coerced. In this research, the PIs argue that the most secure cryptographic credentials are ones of which users aren't even aware. On the basis of this argument, the goal of this research is to investigate a new psychophysiological approach for secure user authentication via non-volitional components of the EEG brainwave response. Specifically, the research will focus on investigation and assessment of users’ involuntary brain behaviors. We will examine both how consistent these responses are over time and how unique they are to individuals. By identifying reproducible, unique features of the EEG, we will develop a method to accurately recognize and authenticate people by extracting “brain passwords”. The research combines disciplines (cognitive science, cognitive neuroscience, bioengineering, and cyber-security) to address open theoretical questions in cognitive neuroscience and open applied challenges in cyber-security.
Huiyang Li (Systems Science and Industrial Engineering), Ann Myers (Decker School of Nursing), Zhanpeng Jin (Electrical and Computer Engineering)
“Developing a Fall Prevention and Alerting System for Nursing Homes"
Falls occur frequently and repeatedly among nursing home residents and cause about 1800 deaths each year. By 2020, the annual direct and indirect cost of fall injuries is expected to reach $54.9 billion. Although auto-alert systems have been developed to detect and report falls of the elderly in the community, fewer were developed for the nursing home setting and are not suited for this setting due to the unique challenges in nursing homes. The objective of this project is to (1) conduct interviews, observations, work analysis and surveys in nursing homes to understand the challenges in fall prevention, staff workflow, and the strategies they employed to manage emergency situations; (2) develop a fall monitoring system for nursing homes that focuses on prediction/prevention. The system will incorporate smart sensors for residents, fall prediction algorithm, mobile devices for staff, and multi-modal (visual, auditory/voice and tactile) interfaces. A prototype of the system will be evaluated through laboratory experiments and eventually field tests, and is expected to reduce falls.