Project Summary / Abstract
Upper-limb paresis is the most common impairment following a stroke affecting 75% of stroke survivors,
which can be more prominent in one of the two limbs. Most recovery of functional impairments occurs within
the first few weeks after stroke and plateaus thereafter. Unfortunately, even after patients reach a stable phase
of recovery, their functional level of the stroke-affected limb may decline. Therefore, it is clinically important to
maintain the regained functional level beyond the first couple weeks of spontaneous recovery by continuing to
practice the use of the affected limb during daily living.
Wearable technologies have emerged as a low-cost, objective tool to monitor the performance of the upper
limbs during activities of daily living (ADLs). However, to date, there exists no study that has investigated the
effectiveness of a mobile-health (mHealth) system aiming to enable high-dosage motor performance in chronic
stroke survivors in the real-world setting. Specifically, the optimal configuration of the goal setting, feedback
mechanism and ways to share data among the stakeholders (patients and clinicians) remains unknown.
This proposal aims to develop and validate an mHealth technology that aims to encourage affected limb
use during the performance of ADLs in chronic stroke survivors. To accomplish this goal, we will employ the
unique finger-worn ring sensor (accelerometer), developed by our academic-industry partnership, that can
capture both gross-arm and fine-hand use of the limbs that are essential in the performance of ADLs. We will
study important aspects of making positive behavior changes to encourage the affected limb use by fully
leveraging the computational insights drawn from sensor data combined with clinical insights from providers.
To accomplish this research goal, Aim 1 will focus on the development of an mHealth platform, composed of
body-networked sensors and cloud-based systems, to monitor the real-world use of the limbs in chronic stroke
survivors. In Aim 2, we will develop machine-learning based algorithms to extract clinically meaningful
information regarding real-world upper limb use from sensor data. Aim 3 will investigate the optimal design of
our mHealth system – such as individual tailoring of the goal, design of the feedback, medium and timing to
deliver feedback, and ways to share data among the stakeholders (patients and clinicians) – via human-centered
design approaches. Finally, in Aim 4, we will validate the short-term (8 weeks) effectiveness of the mHealth
system in improving the use of the affected limb through a field deployment study.
We believe that outcomes of this project will open a new door leading to previously unexplored datasets and
understanding of patient-technology interactions to promote positive behavior changes to enable a high dosage
of physical and occupational therapy, which can form the basis of a wide range of future investigations of
hemiparesis rehabilitation and personalized disease management.
