Collaboration with Other Institutions Component

与其他机构的合作部分

• 批准号: 10646512
• 负责人: Arun Jayaraman
• 金额: $ 16.3万
• 依托单位: REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646512
• 关键词: Acceleration; Address; Algorithms; Amputation; Android; Area; Arthritis; Benchmarking; Biochemical; Biological Markers; Biosensing Techniques; Brain Injuries; Calibration; Caring; Categories; Cellular Phone; Chronic stress; Client; Clinic; Clinical; Cognitive; Collaborations; Collection; Communities; Computers; Data; Data Analyses; Data Collection; Databases; Devices; Disabled Persons; Disease; Elderly; Electronics; Engineering; Focus Groups; Goals; Health; Hydration status; Hydrocortisone; Impairment; Individual; Institution; Internet; Intuition; Label; Laboratories; Location; Malignant Neoplasms; Measurement; Measures; Mechanics; Microfluidics; Monitor; Movement; Musculoskeletal; Neuromuscular conditions; Occupations; Operating System; Outcome; Outcome Measure; Parkinson Disease; Patients; Performance; Persons; Physical Medicine; Physical Performance; Physiological; Physiology; Pilot Projects; Population; Preparation; Protocols documentation; Rehabilitation Outcome; Rehabilitation therapy; Research Personnel; Resources; Scientist; Semiconductors; Site; Skin; Spinal Injuries; Spinal cord injury; Standardization; Stress; Stroke; System; Tablets; Technology; Testing; Thinness; Traumatic Brain Injury; Traumatic Brain Injury recovery; Work; biomechanical test; chronic pain; cohort; data standards; electronic sensor; experience; experimental study; flexibility; improved; laptop; light weight; mobile application; new technology; prediction algorithm; rehabilitation research; rehabilitation technology; research and development; sabbatical; sensor; smart watch; stroke survivor; technique development; technology development; temporal measurement; wireless

Project Summary—Technology Development Based on the collective experiences of the clinicians, scientists, engineers, and patient collaborators who comprise the Center for Smart use of Technologies to Assess Real-world Outcomes (C-STAR), we propose three specific aims with the primary goals of: (1) addressing the need for laboratory, clinical, and community assessment, (2) providing a resource for the rehabilitation research community, (3) extending technologies for which we have significant preliminary data, and (4) providing resources for use C-STAR clients during Pilot Studies, sabbaticals, or other sponsored collaborative activities. We have previously developed and tested a new class of epidermal electronic sensor (EES)-based technologies that has tremendous potential to track real-world outcomes for rehabilitation researchers. EES- based technologies package conventional inorganic semiconductor technologies into thin, lightweight, mechanically `soft' (i.e., flexible, stretchable) devices that provide advanced, wireless biosensing capabilities. Epifluidic devices integrate electronic components with microfluidic sweat collection systems to enable non- invasive, continuous monitoring of sweat dynamics (loss, instantaneous rate, and average rate), biochemical composition, and physiology, skin health, and hydration. For Aim 1, we will add the capacity for real-time measurement of cortisol levels in sweat to this sensor. Many technologies, such as smart watches or mobile phones, generally have many capabilities and are easy to use. Although the raw data measured with such technologies (accelerations, angular velocities, barometric readings, etc.) are of high quality, the algorithms used to interpret these data do not translate well for individuals with disability. It is critical to calibrate mobility prediction algorithms using properly labelled, condition-specific data collected from individuals with disability. For Aim 2, we will convene expert panels of clinicians, scientists, and users to create standardized protocols for collecting labelled “benchmark” sensor data specific to stroke survivors, persons with spinal cord injury, traumatic brain injury, or Parkinson's disease. We will then collect labelled activity data from mobile phones, smart watches, and inertial sensors from cohorts of individuals with these conditions to generate a publicly available, online database. The Rehabilitation Measures Database (RMD) is a leading resource for benchmarks and outcomes, featuring more than 400 measures supported by doctors, clinicians, therapists, and rehabilitation researchers and achieving an average of 11,000 hits per day. While the site works well for laptop and desktop computers, improvements would allow access to RMD in the field using smart phones and tablets. For Aim 3, we will develop a RMD application (app) with an intuitive user interface that can be used with Android and iOS operating systems. These aims build on our current technologies to generate resources that will be of immense value to the rehabilitation research community.

项目概要——技术开发 基于信徒、科学家、工程师和患者合作者的集体经验 包括智能利用技术评估现实世界成果中心 (C-STAR)，我们建议 三个具体目标，主要目标是：(1) 满足实验室、临床和社区的需求 评估，(2) 为康复研究界提供资源，(3) 扩展技术 我们拥有重要的初步数据，以及 (4) 为试点期间使用 C-STAR 客户提供资源 学习、休假或其他赞助的合作活动。 我们之前开发并测试了一种基于表皮电子传感器 (EES) 的新型传感器 这些技术在跟踪康复研究人员的现实世界结果方面具有巨大潜力。 基于技术将传统的无机半导体技术封装成薄型、轻质、 提供先进的无线生物传感功能的机械“软”（即灵活、可拉伸）设备。 流控装置将电子元件与微流控汗液收集系统集成在一起，以实现非 侵入性、连续监测汗液动态（损失、瞬时速率和平均速率）、生化 对于目标 1，我们将添加实时功能。 该传感器测量汗液中的皮质醇水平。 许多技术，例如智能手表或手机，通常具有许多功能并且易于使用 尽管使用此类技术测量的原始数据（加速度、角速度、气压） 读数等）是高质量的，用于解释这些数据的算法不能很好地转化为个人 使用正确标记的、针对具体情况的校准移动预测算法至关重要。 为了实现目标 2，我们将召集由信徒、科学家、 和用户创建协议，用于收集特定于中风标准化的标记“基准”传感器数据 然后我们将收集幸存者、脊髓损伤、脑外伤或帕金森病患者。 来自手机、智能手表和惯性传感器的标记活动数据，来自人群 这些条件生成一个公开可用的在线数据库。 康复措施数据库 (RMD) 是基准和结果的领先资源，具有 由医生、牧师、治疗师和康复研究人员支持的 400 多项措施 虽然该网站在笔记本电脑和台式电脑上运行良好，但每天平均点击量为 11,000 次。 改进将允许使用智能手机和平板电脑在现场访问 RMD，我们将开发 Aim 3。 具有直观用户界面的 RMD 应用程序 (app)，可与 Android 和 iOS 操作系统一起使用。 这些目标建立在我们现有技术的基础上，以产生对人类具有巨大价值的资源。 康复研究团体。