A Smartphone Framework for Mild Cognitive Impairment Assessment

用于轻度认知障碍评估的智能手机框架

• 批准号: RGPIN-2018-06041
• 负责人: McLeod, Robert
• 金额: $ 4.08万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752193
• 关键词: Smartphone; Framework; Mild; Cognitive; Impairment

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.

我的计划是与新的数学和统计模型，理论，方法和分析工具的发展，有可能导致未来在人类健康中的应用，如在诊断和治疗与年龄相关的认知健康问题。具体来说，我的研究是通过严肃的游戏建立认知健康评估的计算机技术。 我的发现补助金（DG）主题是设计和开发原始的移动的严重游戏，用于使用机器学习（ML）进行轻度认知障碍（MCI）评估。我的研究是一个计算机工程项目，探索认知功能，衰退，“记忆滑坡”，“集合丢失”和在简单的严肃游戏中无法开发和/或记住策略之间的关系。我将开发一个强大的软件平台来评估玩家识别、应用和记住简单手机游戏统计获胜策略的能力，并开发分析来评估记忆下降。这项工作的长期目标或挑战将是提供吸引人和娱乐性的机制，通过这种机制，在诊断出更典型和明显的认知衰退症状之前，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，以应用于MCI评估。目标二：开发和优化机器学习引擎和技术，对玩家行为进行聚类和分类。目标3：探索其他传感器融合，为MCI评估提供额外功能。总体而言，这些定义研究计划的目标将进一步确立简单手机游戏在认知评估中的作用。一个强大的框架，将有助于建立信誉，我的论文提供了一个平台，探讨“手机游戏MCI评估”。这个框架将帮助我们和其他人更好地理解它的概念深度以及认知健康评估的严肃游戏的局限性。该计划还将为6个新的HQP提供令人兴奋的机会。使用智能手机游戏进行明确的认知健康评估的概念是独特的，因为它没有在临床实践中使用，尽管研究人员意识到它的潜力。我的游戏是不同的，因为它不是简单地计算机化一个更多的基于纸张的评估方法，也不是依赖于困难的过程，如空间认知，而是简单和熟悉的过程，如订购卡，并回忆简单的策略。我的游戏是迭代的，玩得相当快，可以在20分钟左右的时间内收集大量的数据。最简单的数据基本上是阳性、假阳性、阴性和假阴性，它们构成了ML方法的输入。这里申请的DG部分是为了开发用于处理此类数据的ML方法。加拿大的好处是认知健康评估工具的变革性改进。