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