Detecting cognitive impairment using keystroke and linguistic features of typed text: toward an adaptive method for continuous monitoring of cognitive status

Authors:
Lisa M. Vizer;Andrew Sears
Affiliations:
Information Systems Department, UMBC, Baltimore, MD;B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY
Venue:
USAB'11 Proceedings of the 7th conference on Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society: information Quality in e-Health
Year:
2011

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Abstract

Perception, attention, and memory form the foundation of human cognition, and are functions that most people take for granted. However, factors such as environment, mood, stress, education, trauma, aging, or disease can impact cognitive function both positively and negatively. For example, working memory capacity generally declines somewhat with age, but a particular individual's accumulated knowledge and skills usually remain intact and can continue to grow. Current methods of monitoring persons for cognitive decline use only normative data and do not take individual differences into account. Given that early intervention can lessen the impact of cognitive decline, concern that current cognitive assessments do not adequately address individual differences, and growing technology use by older adults, this paper investigates a more effective method for monitoring cognitive function using everyday interactions with IT.