This paper uses data analytics to provide a method for the measurement of a key driving task, turn signal usage as a measure of an automatic over-learned cognitive function drivers. The paper augments previously reported more complex executive function cognition measures by proposing an algorithm that analyzes dashboard video to detect turn indicator use with 100% accuracy without any false positives. The paper proposes two algorithms that determine the actual turns made on a trip. The first through analysis of GPS location traces for the vehicle, locating 73% of the turns made with a very low false positive rate of 3%. A second algorithm uses GIS tools to retroactively create turn by turn directions. Fusion of GIS and GPS information raises performance to 77%. The paper presents the algorithm required to measure signal use for actual turns by realigning the 0.2Hz GPS data, 30fps video and GIS turn events. The result is a measure that can be tracked over time and changes in the driver's performance can result in alerts to the driver, caregivers or clinicians as indication of cognitive change. A lack of decline can also be shared as reassurance.

Additional Metadata
Keywords Alzheimer Disease, Cognitive Decline, Cognitive Measurement, Data Analytics
Persistent URL dx.doi.org/10.1109/EMBC.2014.6944438
Conference 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
Citation
Wallace, B. (Bruce), Goubran, R, & Knoefel, F. (Frank). (2014). Measurement of signal use and vehicle turns as indication of driver cognition. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (pp. 3747–3750). doi:10.1109/EMBC.2014.6944438