Detecting Mild Cognitive Impairment Through Digitized Trail-Making Test Interface

Raniero Aaron Lara-Garduno, Yajun Jia, Nicolaas E. Deutz, Marielle Engelen, Nancy Leslie, Tracy Hammond

Keywords: Digital Sketch Recognition, Human-Computer Interaction, Applied Machine Learning, Telehealth, Digital Neuropsychology

TL;DR: We present a tablet interface that is a digitized Trail-Making test (typically used in medicine to gauge cognitive impairment) and we use the digital sketches from it to identify a participants' impairment with machine learning classification.

Abstract: With the number of Alzheimer's patients reaching 5 million in 2014 according to the U.S. Center for Disease Control and Prevention, increasing emphasis has been placed on identifying and understanding its precursor condition, Mild Cognitive Impairment (MCI). MCI is characterized by subtle but abnormal cognitive decline and is challenging to detect without formal testing. Neuropsychologists use paper-and-pencil tests such as the Trail-Making Test (TMT) for diagnosis, and ongoing research places importance on high-granularity sketch data from digital TMTs. We present SmartStrokes, a digital TMT app designed to simulate the paper-and-pencil testing experience on a tablet and stylus. Our contribution frames the principles of digital sketch recognition and Human-Computer Interaction (HCI) into the existing neuropsychological test, outlining the creation of a pair of classification models that identify MCI on an individual segmented line basis. Such a per-line classification method which could provide localized sketching behavior indicative of MCI. We also present an interface for the digital TMT and a refinement of line segmentation algorithms from previous research to better distinguish between the actions that a participant takes when completing the exam.