This paper explores the detection of cognitive change in individuals by sensing a high cognition task (driving). The paper proposes algorithms for the analysis of a set of training trips by a driver to create baseline attributes and features for measurement of baseline navigational performance. Algorithms are proposed for the measurement of subsequent trips through comparison to the baseline performance attributes and the paper shows that trips with common coping mechanisms for cognitive decline can be identified and classified. Common coping mechanisms include use of familiar routes by backtracking to home or reduction in trip complexity through reduction in the variety of stops or in the number of stops are all identified. In addition, algorithms are proposed that identify changes in the navigation ability by indicating routing mistakes or poor choices. The paper shows that the measurement of patient performance can be compared to gold standard Google Maps based routing and navigation choices providing a baseline for a patient's cognitive performance and that cognitive change could be detected in behavior change relative to this baseline including less efficient trip planning, reduced trip complexity or less optimal navigation through use of inefficient but more familiar routes as coping mechanisms.

Additional Metadata
Keywords Alzheimer Disease, Cognitive Decline, Cognitive Measurement
Persistent URL dx.doi.org/10.1109/MeMeA.2013.6549728
Conference IEEE International Symposium on Medical Measurements and Applications, MeMeA 2013
Citation
Wallace, B. (Bruce), Goubran, R, & Knoefel, F. (Frank). (2013). Cognitive change measurement through driving navigation ability sensing and analysis. In MeMeA 2013 - IEEE International Symposium on Medical Measurements and Applications, Proceedings (pp. 164–169). doi:10.1109/MeMeA.2013.6549728