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

项目摘要/摘要 世界人口正在老龄化，患有慢性健康疾病的老年人数量不断增加 这是我们的社会必须应对的挑战。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的知识中 学习和活动学习技术，以设计自动化健康评估和干预策略。 该项目的长期目标是通过以下方式改善人类健康并影响保健服务的提供 开发智能环境，帮助进行健康监测和干预。这样做的主要目标是 应用程序是设计一种“环路中的临床医生”智能家居，使个人能够管理自己的慢性疾病 通过自动化健康监测、评估和干预影响评估来改善健康状况。 在我们之前工作的基础上，方法将是生成描述个人行为的分析 日常使用智能家居、智能手机和活动学习(目标1)。我们训练有素的临床医生将使用 进行健康评估和检测健康事件的分析(目标2)。此外，我们还将推出 脑健康干预措施，以支持可持续改善脑健康(目标3)。最后，我们训练 来自临床观察的机器学习算法，以自动评估健康和干预 影响(目标4)。这些技术的使用有望改善和扩大功能健康和 老年人的福祉，导致更主动和预防性的卫生保健，并减轻照顾者的负担 健康监测和援助的一部分。通过了解影响即刻遵守的情景因素， 遵守情况可以增加。这种方法是创新的，因为它将探索和验证新的 基于临床基础知识的活动学习和健康评估的机器学习技术。这些 贡献是巨大的，因为它们可以通过以下方式扩大我们老龄化社会的健康自我管理 积极主动的医疗保健和实时干预，减轻照顾者的情感和经济负担 和社会。考虑到疗养院护理成本、基于家庭的护理的影响以及人们 注重居家，提高功能独立性，从而支持就地老龄化的技术 虽然提高个人及其照顾者的生活质量对双方都有重要价值 个人和社会。

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 and activity learning technologies to design automated health assessment and intervention strategies. The long-term objective of this project is to improve human health and impact health care delivery by developing smart environments that aid with health monitoring and intervention. The primary objective of this application is to design a “clinician in the loop” smart home to empower individuals in managing their chronic health conditions by automating health monitoring, assessment, and evaluation of intervention impact. Building on our prior work, the approach will be to generate analytics describing an individual's behavior routine using smart homes, smart phones, and activity learning (Aim 1). Our trained clinicians will use the analytics to perform health assessment and detection of health events (Aim 2). In addition, we will introduce brain health interventions to support sustainable improvement of brain health (Aim 3). Finally, we train machine learning algorithms from the clinical observations to automate assessment of health and intervention impact (Aim 4). The use of these technologies is expected to improve and extend the functional health and wellbeing of older adults, lead to more proactive and preventative health care, and reduce the caregiver burden of health monitoring and assistance. By understanding situational factors that impact prompt adherence, adherence situations can be increased. The approach is innovative because it will explore and validate new machine learning techniques for activity learning and health assessment based on clinical ground truth. These contributions are significant because they can extend the health self-management of our aging society through proactive health care and real-time intervention, and reduce the emotional and financial burden for caregivers and society. Given nursing home care costs, the impact of family-based care, and the importance that people place on staying at home, technologies that increase functional independence and thus support aging in place while improving quality of life for both individuals and their caregivers are of significant value to both individuals and society.