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Calibrating Cognitive Health on an iPad
CSAIL: Computer Science and Artificial Intelligence Lab
26 September 2020
Randall Davis, firstname.lastname@example.org
This is an Experiential Learning Project that offers an hourly wage. It requires a serious commitment of 10-12 hours per week during the term and will give you hands-on experience with an application that will see real use, and could make a substantial difference in people's lives. You will be part of a dedicated team and will have the chance to learn about the larger context in which this application sits. Populations around the world are “greying,” i.e., the high end of the age distribution is becoming a larger percentage of the total. This is in part the consequence advances in healthcare that allow people to live longer. But there is also a less pleasant side of this – more and more people are living long enough to be susceptible to the diseases of mental decline (e.g., Alzheimer’s) that occur in the late 60’s and beyond. The toll these diseases take is astonishing. In the US alone it is estimated that 5.8 million people suffer from some form of dementia and their care costs $290 billion annually. This cost is projected to top 1 trillion dollars by 2050. While there is as yet no cure for these ailments, there are ways of slowing the progress of decline. That in turn means that early detection takes on special importance – the earlier the problem can be detected, the earlier steps can be taken toward mitigation. Cognitive decline is typically measured with a battery of tests that are both verbal and written. Our research group – at MIT/CSAIL and Lahey Clinic – has been developing novel versions of traditional pen and paper neuropsychological tests, taking advantage of digital technology to extract considerably more information the test. As one example, our digital version of the traditional maze test has a number of novel properties. Where traditional maze tests indicate simply whether the subject has found the correct path, we can capture and analyze the entire solution process, e.g., pen speed, the behavior at choice points, and other informative measures. To date we have done this with a digitizing ballpoint pen, but have recently started transitioning to an iPad, which offers additional interesting and novel capabilities. Where the paper form of any test is of course static, the dynamic display provided by a tablet makes possible building guidance into the testing app that ensures more valid data collection. As one example, people solving a maze may stop moving the pen in order to look ahead and try to solve the maze visually. When traditional maze tests are administered, the test administrator is supposed to watch for this and correct the subject, urging them to go back to solving it with the pen, in order to make visible their problem solving process. This offers one simple example of the capabilities desired for a next generation testing platform. Our current (very early) iPad maze test tracks the pen movement continually, and if the pen stops for more than 3 seconds, the test display is obscured except for a small circle around the current position of the pen. The patient is informed by the app (aloud) that they have to keep solving the maze with the pen, and the display returns to normal only after they have re-started. A tablet also enables collecting what is known as ecological data, i.e., small behaviors that, while not the major focus of the test, are also indicative of cognitive status. The test routinely gives written instructions about what to do next; unknown to the subject the system measures how long it takes the subject to read and react to those directions. Doing this for one test is the first step toward a testing platform. By a platform we mean an architecture that allows multiple different tests to be given, that make use of this general idea of observing subject behavior in real time, measuring it in subtle ways, and intervening as needed.
Ability to program in Swift. Experience with iPad apps is a big plus, as is any experience with user interfaces, signal processing etc.