Automated Health Assessment through Mobile Sensing and Machine Learning of Daily Activities

通过日常活动的移动传感和机器学习进行自动健康评估

• 批准号: 10472075
• 负责人: Diane Joyce Cook
• 金额: $ 117.85万
• 依托单位: ADAPTELLIGENCE, LLC
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10472075
• 关键词: Activities of Daily Living; Address; Adherence; Adult; Age; Aging; Alzheimer' s disease related dementia; Attenuated; Awareness; Back; Behavior; Caregiver Burden; Caregivers; Caring; Chronic; Clinical; Cognition; Cognitive; Data; Development; Early Diagnosis; Educational Materials; Elderly; Ensure; Environment; Equilibrium; Evaluation; Family; Family health status; Feedback; Future; Goals; Health; Health Care Costs; Health Personnel; Health Status; Health behavior; Home; Home Nursing Care; Impaired cognition; Independent Living; Individual; Insurance; Intelligence; Interdisciplinary Study; Internet; Intervention; Label; Lead; Learning; Life; Link; Machine Learning; Measures; Memory; Methods; Modeling; Moods; Online Systems; Outcome; Outcome Measure; Participant; Patient Self-Report; Pattern; Persons; Phase; Population; Process; Public Health; Quality of life; Reporting; Research; Risk; Sampling; Self Management; Services; Small Business Innovation Research Grant; Societies; Suggestion; System; Technology; Technology Assessment; Testing; Time; Trail Making Test; Visual; Work; activity marker; age related; base; brain behavior; brain health; care costs; care providers; clinical predictors; clinically relevant; commercial application; commercialization; dashboard; design; digital; effective therapy; experience; functional decline; functional independence; health assessment; health care availability; health care quality; improved; innovation; insight; learning strategy; machine learning method; mild cognitive impairment; mobile sensing; new technology; novel; personalized care; personalized medicine; phase 1 study; phase 2 study; physical conditioning; prevent; primary outcome; recruit; research and development; research clinical testing; secondary outcome; sensor; smart watch; success; tool; treatment planning; trend; underserved area; usability; wearable sensor technology

PROJECT SUMMARY / ABSTRACT The world's population is aging and the increasing number of older adults with Alzheimer's disease and related dementias (ADRDs) is a challenge our society must address. While the future of healthcare availability and quality of services seems uncertain, at the same time advances in pervasive computing and intelligent embedded systems provide innovative strategies to meet these needs. Two particular needs which technology can help address is early detection of cognitive and physical decline, and tracking integration of new, healthy brain behaviors into everyday life. The long-term goal for Adaptelligence LLC is to commercialize a smartwatch app, called AcTelligence, to assess a person's cognitive and physical health and to promote healthy brain behavior. The objective of this application is to perform research a development to refine and commercialize a smartwatch app that offers capabilities to detect activities of daily living from smartwatch sensors, extract digital behavior markers from activity-labeled sensor data, predict clinical health measures from behavior markers, and provide user feedback in the form of health status and healthy-behavior prompts. This technology is unique because we consider a person's entire behavior profile and introduce machine learning methods to robustly predict clinical measures from this information. We utilize a popular smartwatch platform to increase accessibility and balance continuous assessment with opportunities to extend and improve health. Building on our successful Phase I effort, our approach is to extract activity-aware digital behavior markers from smartwatch sensor data (Aim 1), automate health assessment based on these markers (Aim 2), and perform participatory design of a web dashboard that provides visual analytics and alerts for brain health (Aim 3). We will validate the sensing and machine learning technologies for a sample of 100 older adults and will refine the interactive analytics through multiple rounds of participatory design with 18 participants. The app will be brought to market through a thorough market analysis and a strategically-designed commercialization plan. The proposed contributions are significant because they will provide insights on cognitive and physical health revealed within a person's everyday environment that promote early detection of cognitive and physical decline that can lead to more effective treatment. This work is important because of the increasing number of older individuals experiencing cognitive and functional limitations due to chronic health conditions. Furthermore, the work addresses the need for individuals to remain functionally independent as long as possible in their own homes, thereby improving quality of life and reducing health care costs.

项目摘要 /摘要 世界的人口正在衰老，老年人患有阿尔茨海默氏病和 相关痴呆症（ADRD）是我们社会必须解决的挑战。而医疗保健可用性的未来 服务质量似乎不确定，同时在普遍计算和智能上进步 嵌入式系统提供了满足这些需求的创新策略。技术的两个特定需求 可以帮助解决的是早期发现认知和身体衰落，并跟踪新的，健康的整合 大脑行为进入日常生活。 Adaptelligence LLC的长期目标是商业化智能手表 应用程序称为Aptligence，以评估一个人的认知和身体健康并促进健康的大脑 行为。该应用的目的是进行研究开发，以完善和商业化 智能手表应用程序提供可检测智能手表传感器日常生活活动的功能，提取 来自活动标记的传感器数据的数字行为标记，可预测行为的临床健康指标 标记，并以健康状况和健康行为提示的形式提供用户反馈。这项技术 之所以独特，是因为我们考虑一个人的整个行为概况，并将机器学习方法介绍给 从此信息中坚固地预测临床指标。我们利用流行的智能手表平台来增加 可访问性和平衡持续评估与扩展和改善健康的机会。建立 我们成功的第一阶段努力，我们的方法是从 智能手表传感器数据（AIM 1），基于这些标记（AIM 2）自动化健康评估，并执行 网络仪表板的参与设计，可为大脑健康提供视觉分析和警报（AIM 3）。我们 将验证100名老年人样本的感应和机器学习技术，并将完善 通过与18个参与者进行多个参与设计的互动分析。该应用将是 通过彻底的市场分析和战略设计的商业化计划进入市场。 拟议的贡献很重要，因为它们将提供有关认知和身体健康的见解 在一个人的日常环境中揭示，以促进认知和身体下降的早期发现 这可以导致更有效的治疗。这项工作很重要，因为较老的数量增加 由于慢性健康状况而遭受认知和功能限制的个人。此外， 这项工作解决了个人必须尽可能长时间保持功能独立的需求 房屋，从而提高生活质量并降低医疗保健成本。