Smart Environment Technologies for Health Assessment and Assistance

描述（由申请人提供）：由于人口老龄化，由于认知或身体障碍而无法在家中独立生活的美国人数量显着增加。目前，在如何应用机器学习技术来改善健康监测以及如何利用这些技术来实施旨在维持独立生活的干预措施方面，知识库存在根本性差距。这项工作的长期目标是通过开发有助于健康监测和干预的智能环境来改善人类健康并影响医疗保健服务。这个特殊的应用程序的目的是设计，实施和评估技术，以评估日常功能的限制，并为早期痴呆症患者提供自动干预策略。对大多数人来说，家是一个避难所，但今天那些需要特殊照顾的人，主要是老年人，必须离开家，以满足临床需要。核心假设是，许多患有认知障碍的老年人可以在自动化辅助和健康监测的帮助下在自己的家中独立生活。拟议工作的理由是，智能环境技术可以改善需要日常功能活动帮助的老年人的生活质量和医疗保健，并减轻护理人员和社会的情感和经济负担。在强有力的初步数据以及计算机科学和临床神经心理学研究人员之间的合作的指导下，我们的中心假设将通过追求以下具体目标进行测试：（1）设计软件算法，使用智能环境数据来识别现实世界中复杂的日常活动，（2）使用智能环境自动进行功能健康评估，并检查基于实验室的措施的生态有效性，（3）设计自动提醒和基于提醒的干预措施，以帮助完成日常活动;（4）分析日常行为模式和生理数据之间的相关性。拟议的工作是创新的，因为它定义了在我们最个人的环境中检测和应对衰老，早期痴呆和残疾的方法：我们的家。拟议的工作是重要的，因为它提供了基础，自动化，强大的功能评估的个人认知能力的限制和干预策略，旨在提高这些人的功能独立性。智能家居技术将使我们能够识别妨碍患者在家中保持独立能力的功能缺陷，而不是依赖于患者或可能会或可能不会与患者长时间相处的线人的自我报告，并通过干预真实的世界环境来延长在家中的独立生活。 公共卫生相关性：这些研究代表了智能环境和机器学习技术在家庭功能性能评估问题上的首次应用。这项研究的结果将是嵌入日常环境中的软件算法，为测量功能限制提供更可靠和更容易获得的工具，并为创建和评估维持或提高完成日常生活任务能力的工具奠定基础。一旦这些技术成为可能，它们就有可能提供一种生态上有效的方法，用于监测个人的日常功能状况，并通过使用自动化和基于监护人的干预措施，延长个人在自己家中独立生活的时间。

DESCRIPTION (provided by applicant): The number of Americans unable to live independently in their homes due to cognitive or physical impairments is rising significantly due to the aging of the population. There is a currently a fundamental gap in the knowledge base concerning how to apply machine learning technologies to improve health monitoring and how to harness these technologies to implement interventions designed to sustain independent living. The long-term objective of this work is to improve human health and impact health care delivery by developing smart environments that aid with health monitoring and intervention. The objective of this particular application is to design, implement, and evaluate technologies for assessing everyday functional limitations and for providing automated intervention strategies for persons with early-stage dementia. To most people home is a sanctuary, yet today those who need special care, predominantly older adults, must leave home to meet clinical needs. The central hypothesis is that many older adults with cognitive impairment can lead independent lives in their own homes with the aid of automated assistance and health monitoring. The rational for the proposed work is that smart environment technologies can improve quality of life and health care for older adults who require assistance with everyday functional activities and reduce the emotional and financial burden for caregivers and society. Guided by strong preliminary data and a partnership between computer science and clinical neuropsychology researchers, our central hypothesis will be tested by pursuing the following specific aims: (1) Design software algorithms that use smart environment data to recognize complex everyday activities in real-world settings, (2) Use smart environments to automate functional health assessment and to examine the ecological validity of laboratory-based measures, (3) Design automated reminder and prompting-based interventions to aid with everyday activity completion, and (4) Analyze correlations between everyday behavioral patterns and physiological data. The proposed work is innovative because it defines methods of detecting and coping with aging, early dementia and disabilities in our most personal environments: our homes. The proposed work is significant because it provides the basis for automated, robust functional assessment of individuals with cognitive limitations and of intervention strategies designed to improve functional independence for these individuals. Rather than relying on self-reporting by the patient or by an informant who may or may not spend extended time with the patient, smart home technologies will allow us to identify functional deficits that impede a patient's ability to maintain independence in their home as they begin to occur, and to extend independent living at home by intervening in a real world setting. PUBLIC HEALTH RELEVANCE: The proposed studies represent the first application of smart environment and machine learning technologies to the problem of in-home functional performance assessment. The result of this research will be software algorithms embedded in everyday environments that provide more reliable and accessible tools for measuring functional limitations, and that lay the foundation for creating and assessing tools that sustain or improve the ability to accomplish everyday tasks of living. Once such technologies become available, there is the likelihood that they can provide an ecologically valid method for monitoring an individual's everyday functional status and for extending the amount of time individuals can live independently in their own homes through the use of automated and reminder-based intervention.