SCH: INT: AURA - Connecting Audio and Radio Sensing Systems to lmprove Care at Home

从医院到家庭的有效护理过渡对于确保对高危患者的最佳护理至关重要。 并降低可避免的重新入院成本，这会给医疗体系带来负担。一种普遍做法 在技术辅助的护理过渡中，是通过网络培训和指导患者和家庭护理人员 以及基于手机的系统，用于症状报告、生命体征监测和提供反馈。 这些制度虽然有效，但有三大局限：(A)病人或照顾者必须积极 测量数据并将其输入系统--这很容易出错、主观，而且经常被遗忘；(B)网络或 基于移动设备的交互可能既繁琐又要求苛刻--例如输入数据、了解健康状况等 状态或获取和响应警报需要在一系列表单/Web中键入和点击 (C)系统通过报文和通知提供的反馈往往无效， 没人注意到。为了克服这些限制，该项目使用数据驱动的方法来开发 家庭环境中的语音支持、情景感知、治疗后自我护理系统。拟议的系统 将(A)使用基于高级WiFi无线电信号的人类在家中监控患者的特定活动 活动识别算法；(B)定制亚马逊等语音助手的自然语言响应 基于患者的位置、活动和健康历史的Echo/Alexa；以及(C)自动化数据输入和 报告以减轻患者或护理者的负担。该系统将部署在40个结直肠和膀胱 癌症患者之家，用于评估可用性、可行性和初步的益处大小。 该项目的总体目标与国家航空航天局2017-2027年的十年战略计划保持一致。 通过语音和WiFi无线电传感器数据的自动收集、链接、管理和建模，这 该项目将创建一个相互关联的数字生物标记物生态系统，这些数字生物标记物可以解释、影响和预测 护理过渡期间的健康结果。通过使用低成本语音助理(不到50美元)和无处不在的 家庭WiFi，它最大限度地向大众传播和参与数据支持的健康。 为了促进未来数据准备就绪的劳动力的发展，三名博士生将在这个领域工作 多学科项目，将开发一个新的mHealth课程，并将开放科学实践 在项目的开发、部署和传播阶段应用。

An effective care transition from hospital to home is crucial to ensure optimal care for high-risk patients and to reduce the cost of avoidable readmissions that burdens the healthcare system. A common practice in technology-assisted care transition is to train and guide patients and family caregivers through the web and mobile phone-based systems for symptom reporting, vital signs monitoring, and providing feedback. Although effective, these systems have three major limitations: (a) the patient or caregiver has to actively measure and enter data into the system-which is error-prone, subjective, and often forgotten; (b) web or mobile-based interactions can be cumbersome and demanding-tasks like entering data, knowing health status, or getting and responding to an alert require typing and clicking through a series of forms/web pages; and (c) feedback from the system through messages and notifications is often ineffective and unnoticed. To overcome these limitations, this project employs a data-driven approach to develop a voice-enabled, context-aware, post-treatment self-care system in home settings. The proposed system will (a) monitor specific activities of a patient at home using advanced WiFi radio signal-based human activity recognition algorithms; (b) tailor natural language responses of voice assistants like Amazon Echo/Alexa based on the patient's location, activity, and health history; and (c) automate data-entry and reporting to reduce patient or caregiver burden. The system will be deployed in 40 colorectal and bladder cancer patients' homes for assessing usability, feasibility, and preliminary magnitude of benefits. The overarching goals of this project are aligned with the NLM's 10-year strategic plan for 2017-2027. Through automated collection, linking, curation, and modeling of voice and WiFi radio sensor data, this project will create an interconnected ecosystem of digital biomarkers that explain, influence, and predict health outcomes during care transition. By using low-cost voice assistants (less than $50) and ubiquitous home WiFi, it maximizes the dissemination and engagement of data-powered health to mass population. To foster the development of a data-ready workforce for the future, three Ph.D. students will work in this multidisciplinary project, a new mHealth course will be developed, and open science practices will be applied during the development, deployment, and dissemination phases of the project.