A Smartphone Framework for Mild Cognitive Impairment Assessment

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My program is related to the development of new mathematical and statistical models, theories, methodologies, and analytical tools with potential to lead to future applications in human healthsuch as in the diagnosis and treatment of age related cognitive health concerns. Specifically, my research is in building computer technology for Cognitive Health Assessment through serious games. My Discovery Grant (DG) theme is designing and developing original mobile serious games for Mild Cognitive Impairment (MCI) assessment using machine learning (ML). My research is a computer engineering program exploring relationships between cognitive function, decline, “memory slips”, “loss of set” and failure to develop and/or remember strategies in simple serious games. I will produce a robust software platform to assess a player's ability to recognize, apply, and remember statistically winning strategies for simple mobile games and develop analytics to assess memory decline. The long term objectives or challenges of this work will be to provide engaging and entertaining mechanisms by which cognitive changes may be inferred from player performance before more typical and obvious symptoms of cognitive decline are diagnosable. Objective 1: Develop and optimize new technology-mediated tools in the form of mobile games for application to MCI assessment. Objective 2: Develop and optimize ML engines and techniques to cluster and classify player behaviors.Objective 3: Explore additional sensor fusion to provide additional features for MCI assessment.Overall, these objectives that define the research program will further establish the role simple mobile games can play in cognitive assessment. A robust framework, will help establish the credibility of my conjectures providing a platform to explore “Mobile Games for MCI assessment”. This framework will help us and others better understand its conceptual depth as well as limitations of serious games for cognitive health assessment. The program will also provide exciting opportunities for 6 new HQP.The notion of using Smartphone games for explicit cognitive health assessment is unique in that is not in used in clinical practice although researchers are aware of its potential. My game is different in that it is not simply computerizing a more paper based assessment method nor is it relying on difficult processes like spatial cognition, but rather simple and familiar processes such as ordering cards, and recalling simple strategies. My game is iterative, with fairly rapid play, and can collect considerable hand by hand data within 20 minutes or so. The simplest data is essentially positives, false positives, negatives, and false negatives, which form the input into ML methods. The DG applied for here is in part to develop the ML methods for processing this type of data. The benefit to Canada is a transformative improvement in tools for cognitive health assessment.