Collaboration with Other Institutions Component

项目概要-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， 包括中心的智能使用技术来评估现实世界的结果（C-STAR），我们建议 三个具体目标，主要目标是：（1）解决实验室，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， 我们有重要的初步数据，（4）在试点期间为使用C-STAR客户提供资源 学习，休假，或其他赞助的合作活动。 我们以前已经开发和测试了一类新的表皮电子传感器（EES）为基础的 这些技术具有巨大的潜力，可以为康复研究人员跟踪现实世界的结果。EES- 的技术将传统的无机半导体技术封装成薄，重量轻， 机械“软”（即，柔性的、可拉伸的）设备，其提供先进的无线生物感测能力。 微流控装置将电子部件与微流控汗液收集系统集成，以实现非接触式汗液收集。 汗液动力学（损失、瞬时速率和平均速率）、生化 成分、生理学、皮肤健康和水合作用。对于目标1，我们将增加实时 测量汗液中的皮质醇水平。 很多技术，比如智能手表或者移动的手机，一般都有很多功能，而且很容易 使用.虽然用这种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并不能很好地翻译给个人 残疾人。使用适当标记的、特定于条件的 从残疾人收集的数据。对于目标2，我们将召集由临床医生、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“基准”传感器数据 幸存者、患有脊髓损伤、创伤性脑损伤或帕金森病的人。我们将收集 来自移动的手机、智能手表和惯性传感器的标记活动数据， 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）是基准和成果的主要资源， 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， 达到平均每天11,000次点击。虽然该网站适用于笔记本电脑和台式电脑， 这些改进将允许使用智能手机和平板电脑在现场访问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，可用于Android和iOS操作系统。 这些目标建立在我们现有的技术基础上，以产生对人类具有巨大价值的资源。 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.