For several decades, doctors have screened for conditions including Parkinson’s and Alzheimer’s with the CDT, which asks subjects to draw an analog clock-face showing a specified time, and to copy a pre-drawn clock. But the test has limitations, because its benchmarks rely on doctors’ subjective judgments, such as determining whether a clock circle has “only minor distortion.” CSAIL researchers were particularly struck by the fact that CDT analysis was typically based on the person’s final drawing rather than on the process as a whole.
Enter the Anoto Live Pen, a digitizing ballpoint pen that measures its position on the paper upwards of 80 times a second, using a camera built into the pen. The pen provides data that are far more precise than can be measured on an ordinary drawing, and captures timing information that allows the system to analyze each and every one of a subject’s movements and hesitations. Some of the machine learning techniques they used were designed to produce “transparent” classifiers, which provide insights into what factors are important for screening and diagnosis. “These examples help calibrate the predictive power of each part of the drawing,” says first author William Souillard-Mandar, a graduate student at CSAIL. “They allow us to extract thousands of features from the drawing process that give hints about the subject’s cognitive state, and our algorithms help determine which ones can make the most accurate prediction.” Read the full article at the MIT News Office