Collaboration with Other Institutions Component

项目摘要-技术开发 基于临床医生、科学家、工程师和患者合作者的集体经验 组成智能使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)推广技术，以 我们有重要的初步数据，以及(4)为在试点期间使用C-STAR客户端提供资源 学习、休假或其他受赞助的合作活动。 我们之前已经开发并测试了一种基于表皮电子传感器(EES)的新型 为康复研究人员追踪现实世界结果的巨大潜力的技术。EES- 基于技术的技术将传统的无机半导体技术包装成薄、轻、 机械“软”(即，灵活的、可伸展的)设备，提供先进的无线生物传感能力。 Epifluic设备将电子元件与微流控汗液收集系统集成在一起，使非 有创性、持续监测汗液动态(丢失、瞬时速率和平均速率)、生化 成分、生理、皮肤健康和水合作用。对于目标1，我们将添加实时能力 测量汗液中的皮质醇水平。 许多技术，如智能手表或移动电话，通常具有许多功能，并且很容易 来使用。尽管用这些技术(加速度、角速度、气压)测量的原始数据 读数等)都是高质量的，用于解释这些数据的算法对个人来说并不能很好地翻译 残障人士。使用适当标记的特定条件来校准移动性预测算法是至关重要的 从残疾人士那里收集的数据。对于目标2，我们将召集临床医生、科学家、 和用户创建标准化协议，用于收集特定于中风的标记的“基准”传感器数据 幸存者、脊髓损伤、创伤性脑损伤或帕金森氏症患者。然后我们会收集 来自移动电话、智能手表和惯性传感器的标记活动数据，来自具有 这些条件才能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 400多项措施得到了医生、临床医生、治疗师和康复研究人员的支持 实现了平均每天11,000次点击。虽然该网站在笔记本电脑和台式电脑上运行良好， 改进将允许在实地使用智能手机和平板电脑访问RMD。对于目标3，我们将开发 RMD应用程序(APP)，具有直观的用户界面，可在Android和iOS操作系统上使用。 这些目标建立在我们现有技术的基础上，以产生对 康复研究社区。

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.