A Smartphone Framework for Mild Cognitive Impairment Assessment

我的项目是关于新的数学和统计模型、理论、方法和分析工具的开发，这些模型、理论、方法和分析工具有可能导致未来在人类健康方面的应用，例如在与年龄相关的认知健康问题的诊断和治疗中。具体地说，我的研究是通过严肃的游戏为认知健康评估构建计算机技术。My Discovery Grant(DG)的主题是使用机器学习(ML)设计和开发用于轻度认知障碍(MCI)评估的原创移动严肃游戏。 我的研究是一个计算机工程程序，探索认知功能、衰退、“记忆失误”、“失落”和在简单严肃的游戏中未能开发和/或记忆策略之间的关系。我将开发一个强大的软件平台来评估玩家识别、应用和记忆简单手机游戏的统计制胜策略的能力，并开发分析来评估记忆力下降。这项工作的长期目标或挑战将是提供引人入胜和有趣的机制，通过这种机制，在更典型和明显的认知能力下降症状被诊断之前，可以从球员的表现中推断出认知变化。 目标1：开发和优化手机游戏形式的新技术中介工具，应用于MCI评估。 目标2：开发和优化ML引擎和技术来对玩家行为进行聚类和分类。 目的3：探索额外的传感器融合，为MCI评估提供额外的特征。 总体而言，定义研究计划的这些目标将进一步确立简单的手机游戏在认知评估中可以发挥的作用。一个强大的框架，将有助于建立我的猜想的可信度，提供一个平台来探索“手机游戏的MCI评估”。这个框架将帮助我们和其他人更好地理解其概念深度以及用于认知健康评估的严肃游戏的局限性。该计划还将为6名新HQP提供令人兴奋的机会。 使用智能手机游戏进行显性认知健康评估的概念是独特的，因为这一概念并未在临床实践中使用，尽管研究人员意识到了它的潜力。我的游戏的不同之处在于，它不是简单的计算机化的纸质评估方法，也不是依赖于空间认知等困难的过程，而是简单而熟悉的过程，如订购卡片和回忆简单的策略。我的游戏是迭代的，玩得相当快，可以在20分钟左右的时间里用手收集相当多的数据。最简单的数据实质上是正、假正、负和假负，它们构成了ML方法的输入。这里申请的DG在一定程度上是为了开发处理此类数据的ML方法。对加拿大的好处是认知健康评估工具的变革性改进。

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.