A clinician-in-the-loop smart home to support health monitoring and intervention for chronic conditions

临床医生在环智能家居，支持慢性病的健康监测和干预

• 批准号: 10367017
• 负责人: Diane Joyce Cook
• 金额: $ 14.41万
• 依托单位: WASHINGTON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10367017
• 关键词: Address; Adopted; Adoption; Adult; Aging; Algorithms; Artificial Intelligence; Attention; Behavior; Behavior monitoring; Behavioral; Bioethics; Caring; Chronic; Clinical; Cognitive; Collaborations; Communities; Complex; Computing Methodologies; Data; Data Analytics; Decision Making; Detection; Development; Dimensions; Early Intervention; Elderly; Environment; Event; Exhibits; Family; Feedback; Future; Generations; Goals; Health; Health Hazards; Health Status; Health Technology; Health behavior; Healthcare; Home; Human; Human Rights; Individual; Informed Consent; Intelligence; Intervention; Knowledge; Machine Learning; Methods; Minority; Minority Groups; Modeling; Monitor; Parents; Participant; Patients; Personal Satisfaction; Persons; Population; Procedures; Process; Proxy; Public Health; Quality of life; Race; Reporting; Research; Rights; Risk; Risk Assessment; Sample Size; Self Determination; Signal Transduction; Societies; Subgroup; Techniques; Technology; Training; Training Technics; Translating; Universities; Validation; Vulnerable Populations; Washington; Work; aging in place; algorithm development; base; behavior change; brain health; cognitive change; cost; course module; design; experience; health assessment; health care delivery; improved; intelligent agent; intervention effect; learning algorithm; learning strategy; low socioeconomic status; machine learning algorithm; member; mindfulness; multidisciplinary; novel; online course; open source; parent project; physical conditioning; sensor; smart home; smart watch; tool

PROJECT SUMMARY/ABSTRACT The world’s population is aging and the increasing number of older adults with chronic health conditions is a challenge our society must address. While the idea of smart environments is now a reality, there remain gaps in our knowledge about how to scale smart homes technologies for use in complex settings and to use machine learning (ML) and activity learning technologies to design automated health assessment and intervention strategies. The long-term objective of the parent project is to improve human health and positively impact health care delivery by developing smart environments that aid with health monitoring and intervention. The primary objective of this supplement project is to extend the design of our ‘clinician-in-the-loop’ smart home to include computational methods for recognizing and reporting on bias in ML algorithms. Bias in ML algorithms is a public health hazard. A secondary objective is to add to the scarce body of knowledge regarding ML techniques for assessing and quantifying algorithm bias. In this supplement study, we will demonstrate how to operationalize bioethics principles such as autonomy (the right to make informed decisions about care and right to be informed about any associated risks) into actionable design requirements to close the gap between principles and practice. We will then share these with the ML healthcare application (ML- HCA) research community by creating a new open source online course containing 3 modules. This supplement proposal builds upon our extensive experience in the CASAS lab at Washington State University and prior collaboration on the development of smart home design, activity recognition, and the use of these technologies for functional health monitoring, assessment, and intervention. This supplement effort will allow us to expand our sample size and enhance the robustness of pervasive-computing-based behavior monitoring and machine learning to further advance the quality of health monitoring, including bias recognition. We will offer new advances in computational methods for anti-bias algorithm development using data from smart homes and smart watches that are monitoring the daily activities of underrepresented persons. Our course modules will support knowledge development regarding how to operationalize bioethics principles for practice at each stage of ML-HCA design. In the online course we will also address the importance and impact of multidisciplinary teams in designing for transparency, fairness, and non-discriminatory ML-HCA applications such as the health smart home. Given that ML models are known to introduce bias and risk to members of minority groups and given our national conversation on equity, fundamental human rights, and the risks associated with living as a minority person, it is critical that health technology-human interface with underrepresented persons is improved. Anti-bias ML training techniques that could be broadly applied in the development of ML-HCAs would be of significant value to minority individuals and families and all of society.

项目总结/摘要 世界人口正在老龄化，越来越多的老年人患有慢性健康状况， 我们社会必须面对的挑战。虽然智能环境的想法现在已经成为现实，但仍然存在差距 在我们关于如何扩展智能家居技术以用于复杂环境和使用 机器学习（ML）和活动学习技术，用于设计自动化健康评估， 干预策略。母项目的长期目标是改善人类健康， 通过开发有助于健康监测和干预的智能环境来影响医疗保健的提供。 这个补充项目的主要目标是扩展我们的'临床医生在环'智能设计 主页包括用于识别和报告ML算法中偏差的计算方法。ML中的偏倚 算法是一种公共健康危害。第二个目标是增加稀缺的知识 关于评估和量化算法偏差的ML技术。在本补充研究中，我们将 展示如何实施生物伦理学原则，如自主权（知情决策权 关于护理和被告知任何相关风险的权利）纳入可操作的设计要求， 原则与实践之间的差距。然后，我们将与ML医疗保健应用程序（ML- HCA）研究社区通过创建一个新的开源在线课程，包含3个模块。这 一个补充方案是基于我们在华盛顿州立大学CASAS实验室的丰富经验 以及在智能家居设计、活动识别以及这些技术的使用方面的合作 功能性健康监测、评估和干预技术。这一补充努力将使 我们扩大我们的样本量，增强基于普适计算的行为监测的鲁棒性 和机器学习，以进一步提高健康监测的质量，包括偏见识别。我们将 使用smart数据，为反偏算法开发提供计算方法的新进展 家庭和智能手表正在监测代表性不足的人的日常活动。我们的课程 模块将支持有关如何将生物伦理学原则付诸实践的知识发展 在ML-HCA设计的每个阶段。在在线课程中，我们还将讨论以下方面的重要性和影响： 多学科团队设计透明、公平和非歧视性的ML-HCA申请 比如健康智能家居。鉴于ML模型已知会给成员带来偏见和风险， 少数群体，并考虑到我们关于公平，基本人权和风险的全国性对话， 与作为少数民族的生活有关，至关重要的是，卫生技术与人类的互动， 代表性不足的人的情况有所改善。反偏见ML训练技术，可广泛应用于 制定ML-HCA对少数群体个人和家庭以及整个社会都具有重要价值。

项目成果

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis.

自动化的智能家居评估以支持疼痛管理：多种方法分析。

• DOI: 10.2196/23943
• 发表时间: 2020-11-06
• 期刊: Journal of medical Internet research
• 影响因子: 7.4
• 作者: Fritz RL;Wilson M;Dermody G;Schmitter-Edgecombe M;Cook DJ
• 通讯作者: Cook DJ

Culturally Safe Didactic Dialogue Circles: Student and Cultural Community Leader Engagement.

文化安全的教学对话圈：学生和文化社区领袖的参与。

• DOI: 10.3928/01484834-20190321-15
• 发表时间: 2019
• 期刊: The Journal of nursing education
• 作者: Nguyen-Truong,ConnieKimYen;Closner,Michelle;Fritz,RoschelleL
• 通讯作者: Fritz,RoschelleL

Piloting Smart Home Sensors to Detect Overnight Respiratory and Withdrawal Symptoms in Adults Prescribed Opioids.

试点智能家居传感器检测成人处方阿片类药物的夜间呼吸和戒断症状。

• DOI: 10.1016/j.pmn.2022.08.011
• 发表时间: 2023
• 期刊: Pain management nursing : official journal of the American Society of Pain Management Nurses
• 作者: Wilson,Marian;Fritz,Roschelle;Finlay,Myles;Cook,DianeJ
• 通讯作者: Cook,DianeJ