mDOT TR&D3 (Translation): Translation of Temporally Precise mHealth via Efficient and Embeddable Privacy-aware Biomarker Implementations

mDOT TR

• 批准号: 10541810
• 负责人: Emre Ertin
• 金额: $ 28.14万
• 依托单位: UNIVERSITY OF MEMPHIS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10541810
• 关键词: Address; Algorithms; Architecture; Awareness; Behavioral; Biological Markers; Cellular Phone; Chronic Disease; Clinic; Cloud Computing; Collaborations; Collection; Communication; Communities; Companions; Computational Technique; Computer software; Computing Methodologies; Data; Data Collection; Development; Devices; Digital biomarker; Disease Outcome; Eating; Ensure; Fatigue; Feedback; Frequencies; Generations; Health; Healthcare; Image; Individual; Infrastructure; Intelligence; Intervention; Lead; Life; Life Cycle Stages; Machine Learning; Medical; Medicine; Metadata; Methodology; Methods; Modality; Motion; Noise; Observational Study; Ohio; Participant; Patients; Personally Identifiable Information; Physiological; Physiology; Principal Investigator; Privacy; Research; Research Activity; Research Design; Research Personnel; Resource-limited setting; Resources; Risk; Running; Sampling; Services; Side; Signal Transduction; Smoking; Software Design; Stream; Stress; Structure; System; Technology; Time; Training; Translations; Trust; Universities; Work; biopsychosocial; cloud platform; craving; data reduction; data sharing; denoising; design; healthy lifestyle; high dimensionality; improved; mHealth; machine learning algorithm; mobile computing; multiple chronic conditions; next generation; open source; point of care; prevent; prototype; public health relevance; radio frequency; reconstruction; research study; sensor; signal processing; software development; synergism; technological innovation; technology research and development; tool; usability; wearable device; wearable platform; wearable sensor technology

Principal Investigator: Kumar, Santosh TR&D3: Translation of Temporally Precise mHealth via Efficient and Embeddable Privacy-aware Biomarker Implementations Lead: Dr. Emre Ertin, The Ohio State University; 10% effort (1.2 CM) Abstract: The mHealth Center for Discovery, Optimization & Translation of Temporally-Precise Interventions (the mDOT Center) will enable a new paradigm of temporally-precise medicine to maintain health and manage the growing burden of chronic diseases. The mDOT Center will develop and disseminate the methods, tools, and infrastructure necessary for researchers to pursue the discovery, optimization and translation of temporally- precise mHealth interventions. Such interventions, when dynamically personalized to the moment-to-moment biopsychosocial-environmental context of each individual, will precipitate a much-needed transformation in healthcare by enabling patients to initiate and sustain the healthy lifestyle choices necessary for directly managing, treating, and in some cases even preventing the development of medical conditions. Organized around three Technology Research & Development (TR&D) projects, mDOT represents a unique national resource that will develop multiple methodological and technological innovations and support their translation into research and practice by the mHealth community in the form of easily deployable wearables, apps for wearables and smartphones, and a companion mHealth cloud system, all open-source. TR&D3 will develop, validate and disseminate algorithms, tools and software/hardware designs for translation of temporally-precise mHealth interventions through resource efficient, real time, low-latency and privacy-aware implementation of an array of digital biomarkers that can be deployed at scale. Our approach is centered around a hierarchical computing framework that reduces the data into minimal modular abstractions called Micromarkers computed at the edge devices (Aim 1). Modular Micromarker abstractions are used to compress task-specific information relevant to biomarker computations at the edge devices while stripping nuisance variables such as hardware biases/drifts and background levels not pertinent to inference. Our hierarchical computing framework can be extended to implement high data rate sensor arrays at edge devices to be used at new point of care and ambulatory settings. This is accomplished through integrating a compressive sensing pre-processor to achieve signal acquisition in a resource constrained setting (Aim 2). Finally, TR&D3 will create computational mechanisms and a general biomarker privacy framework to enable participant control over the privacy-utility trade-offs during study design, data collection, and sharing of collected mHealth data for third party research when data cross trust domains (Aim 3). These technologies will be developed in collaboration with collaborative projects and will be disseminated to service projects to ensure that TR&D3 technologies can solve real problems facing the health research community and ensure the usability of these technologies by investigators who are external to the mDOT investigating team. TR&D3 will synergistically work in partnership with the other TR&D projects, the Training and Dissemination Core, and the Administration Core to maximize the societal impact of TR&D3 technologies. 1

主要调查者：库马尔，桑托什 TR&D3：通过高效和可嵌入的隐私感知来翻译时间上精确的mHealth 生物标记物的实现 领先：俄亥俄州立大学Emre Ertin博士；10%努力(1.2 CM) 摘要：移动健康中心发现、优化和翻译时间精确干预措施 (MDOT中心)将实现一种时间精准医学的新范式，以维持健康和管理 慢性病带来的负担越来越大。MDOT中心将开发和传播方法、工具、 和基础设施，为研究人员进行发现、优化和翻译临时- 精确的移动健康干预。这种干预，当动态地个性化到每一时刻时 每个人的生物-心理-社会-环境背景，将在 医疗保健，使患者能够开始并维持直接 管理、治疗，在某些情况下甚至阻止医疗条件的发展。有条理的 围绕着三个技术研发项目，MDOT代表着一个独特的国家 将开发多种方法和技术创新并支持其翻译的资源 以易于部署的可穿戴设备、应用程序的形式由mHealth社区进行研究和实践 可穿戴设备和智能手机，以及配套的mHealth云系统，都是开源的。 TRD3将开发、验证和传播用于翻译的算法、工具和软件/硬件设计 通过资源高效、实时、低延迟和隐私感知在时间上精确的移动健康干预 实施一系列可大规模部署的数字生物标记物。我们的方法是围绕 一种层次化计算框架，将数据简化为最小的模块抽象，称为微标记 在边缘设备上计算(目标1)。模块化微标记抽象用于压缩特定于任务的 与边缘设备处的生物标记物计算相关的信息，同时去除干扰变量，例如 硬件偏差/漂移和背景电平与推断无关。我们的分层计算框架 可以扩展为在边缘设备上实施高数据速率传感器阵列，以用于新的护理点和 可移动设置。这是通过集成压缩传感预处理器来实现的 在资源受限的情况下获取信号(目标2)。最后，tr&d3将创建计算性 允许参与者控制隐私效用的机制和通用生物标记物隐私框架 在研究设计、数据收集和为第三方研究共享收集的mHealth数据期间的权衡 当数据跨越信任域时(目标3)。 这些技术将与合作项目合作开发，并将传播到 服务项目，确保tr&d3技术能够解决健康研究面临的实际问题 社区，并确保MDOT之外的调查人员使用这些技术 调查组。R&D3将与其他R&D项目、培训和 传播核心和行政核心，以最大限度地发挥tr&d3技术的社会影响。 1