Creating adaptive, wearable technologies to assess and intervene for individuals with ADRDs

项目摘要/摘要 机器学习的进步和低成本、可穿戴的传感器为理解、 评估和干预日常生活中的阿尔茨海默病和相关痴呆(ADRDS)。我们 建议创建一个Behaviorome研究计划，该计划将为建筑创造开创性的方法 通过使用机器学习映射行为模式，从可穿戴传感器数据建立健康预测模型 和普适计算技术。该计划将创建创新的多学科想法，以解决 NIH ADRD里程碑11.c，将可穿戴技术/普及计算嵌入现有和新的临床 研究。我们的研究计划建立在各领域跨学科研究贡献的历史基础上 包括从纵向传感器数据进行人类行为建模，以及设计新的评估和 干预机制。我们建议设计和验证用于绘制人类行为组的方法 从行为标记物、扩展技术自动评估认知和功能健康 通过机器学习，将健康和行为与它们的影响联系起来，并用 自动化干预。同样，我们的指导计划建立在对学生的经验培训和早期- 职业研究人员将成为老年技术领域的领导者。我们将招聘和培养毕业生 学生和早期研究人员，包括来自代表性不足群体的学生和早期研究人员，以发展一种机构 多学科行为研究方案，并建立新的研究方案 这是一个具有针对性的里程碑。我们将通过建立网络研讨会系列和创建 YouTube视频，重点介绍和解释Behaviorome设计和应用方面的突破 研究。该计划的结果将包括脚本和模板，以构建具有资源的行为组- 有限的可穿戴设备，将数据和型号扩展到大量不同的人群，将数据与多个 信息来源(例如，遗传学)，自动化健康评估和干预，并创建可理解的 数据和模型的解释。这些将有助于现有的临床研究，如临床医生-在- 循环智能家居、数字存储笔记本和普适计算的功能性能衡量标准。 此外，它们将导致新的临床研究，正式确定健康和疾病之间的联系。 探讨种族和建筑环境对健康的影响，以及ADRD的设计 对服药依从性、任务提示和消极互动的干预降低了事态的升级。建议数 贡献是重大的，因为它们将提供对在 使用可穿戴式传感和普适计算方法的人的日常环境 在以前的工作中进行了调查。此外，指导步骤将为新一代 研究人员提供改进的方法来解决了解、评估和干预ADRDS的需求 在日常环境中，从而提高生活质量和降低医疗保健成本。

PROJECT SUMMARY / ABSTRACT Advances in machine learning and low-cost, wearable sensors offer a practical method for understanding, assessing, and intervening for Alzheimer's Disease and Related Dementias (ADRDs) in everyday spaces. We propose to create a Behaviorome research program that will create ground-breaking methods for building health-predictive models from wearable sensor data by mapping patterns of behavior using machine learning and pervasive computing technologies. This program will create innovative multidisciplinary ideas to address NIH ADRD Milestone 11.c, Embed wearable technologies/pervasive computing in existing and new clinical research. Our research program builds on a history of interdisciplinary research contributions in areas including human behavior modeling from longitudinal sensor data and design of novel assessment and intervention mechanisms. We propose to design and validate methods for mapping a human behaviorome “in the wild”, automatically assessing cognitive and functional health from behavior markers, scaling technologies through machine learning, linking health and behavior with their influences, and closing the loop with automated interventions. Similarly, our mentoring program builds on experience training students and early- career investigators to become leaders in the field of gerontechnology. We will recruit and train graduate students and early-stage researchers, including those from underrepresented groups, to grow an institutional multidisciplinary Behaviorome research program and to establish new research programs that contribute to the targeted Milestone. We will scale the impact of mentoring by establishing a webinar series and creating youtube videos that highlight and explain breakthroughs in the design and application of Behaviorome research. Results of this program will include scripts and templates to construct a behaviorome with resource- limited wearable devices, scale data and models to large diverse populations, integrate data with multiple information sources (e.g., genetics), automate health assessment and intervention, and create understandable explanations of data and models. These will contribute to existing clinical studies such as the clinician-in-the- loop smart home, digital memory notebook, and pervasive computing measures of functional performance. Furthermore, they will lead to new clinical studies that formalize connections between health and its influences, exploration of the impact of ethnicity and the built environment on health, and the design of ADRD interventions for medication adherence, task prompting, and negative interaction de-escalation. The proposed contributions are significant because they will provide insights on detecting and assessing ADRDs within a person's everyday environment using wearable sensing and pervasive computing methods that have not been investigated in prior work. Additionally, the mentoring steps will pave the way for a new generation of researchers to offer improved methods of addressing the need to understand, assess, and intervene for ADRDs in everyday settings, thereby improving quality of life and reducing health care costs.