# Collaboration with Other Institutions Component > **NIH NIH P2C** · REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB · 2022 · $163,000 ## Abstract 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... ## Key facts - **NIH application ID:** 10405437 - **Project number:** 5P2CHD101899-03 - **Recipient organization:** REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB - **Principal Investigator:** Arun Jayaraman - **Activity code:** P2C (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $163,000 - **Award type:** 5 - **Project period:** 2020-05-01 → 2025-04-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10405437 ## Citation > US National Institutes of Health, RePORTER application 10405437, Collaboration with Other Institutions Component (5P2CHD101899-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10405437. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*