Mobile context-aware cognitive testing system

Abstract

Traditional cognitive testing for older adults can be inaccessible, expensive, and time consuming. The development of computerized cognitive tests (CCTs) has made strides to alleviate such issues with traditional cognitive testing. Self-administered CCTs allow for individuals to test rapidly and conveniently on various devices. However, such tests may not factor in relevant contextual information pertinent to the testing situation (e.g., is the user in a proper environment or context to test?). This dissertation aims to develop a mobile, context-aware cognitive testing system (CACTS) capable of tracking and analyzing contextual information during CCTs. By utilizing mobile device sensors and user input, the proposed context-aware system will capture ambient and behavioral data during testing to compliment user performance results. This research will help provide insight into the contextual factors that are relevant to the user's testing efficacy and performance in CCTs.