Biomechanical Models and Objective Metrics for Spasticity Rehabilitation

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Spasticity is a chronic neuromuscular disorder due to brain or nerve damages that affects roughly 12 million people worldwide. It is manifested as an abnormal muscle stiffness that causes pain and interferes with movement, impacting balance and daily functions. Evaluating the severity of spasticity is a crucial step in selecting appropriate treatments for patients to prevent progressive deformities. However, current clinical evaluations rely on ratings based on perception. The subjective ratings are known to be inconsistent and not sensitive and as such fail to accurately monitor the effects of medications or therapies. To facilitate better diagnosis, this project aims to deliver biomechanical metrics for evaluating spasticity with high accuracy through a portable sensor glove system. The sensitive, consistent measurements will allow clinicians to inspect whether a therapy effectively relieves spasticity symptoms. The portable system will also facilitate frequent remote assessments to enable timely interventions, reduce costs, and improve patient care. Throughout this duration of this project, the investigators and students will host annual AccessAbility Fair at Rady Children’s Hospital to provide demonstrations and to disseminate resources for local San Diego children with physical disabilities and conduct STEM outreach activities in local communities.The goal of this project is to capture the dynamic characteristics of spastic muscles through a multi-model sensor glove, in order to gain new insights into the physical mechanisms of spasticity. The clinicians would wear the sensor glove while carrying out standard evaluation maneuvers to measure a patient’s muscle resistance and movement trajectories. This wearable tool is compact and user friendly, unlike prior biomechanical devices that are complex to operate and limited by unwieldy, constraining sensors that pose safety and clinical adoption barriers. This project will carry out key feasibility studies to monitor patient cohorts undergoing routine treatments. The first objective is to characterize spastic muscles through biomechanical models, and thereby quantitatively connect the effects of treatments to changes in specific biomechanical properties. The research will lead to knowledge concerning which biomechanical properties are the most relevant for establishing an objective scale to track the management of spasticity over time. The second objective is to improve measurement standardization through supervised machine learning in combination with a robotic standard, to mitigate artifacts and maximize the inter- and intra-rater consistency in monitoring spasticity. The third objective is to facilitate point-of-care measurements by caregivers, to enable at-home assessment that closely tracks the patients’ response to therapies outside of clinical visits. The system user interface and analysis algorithms will be developed in close collaboration with the rehabilitation clinician team and caregivers, to dismantle the barriers to practical clinical use and facilitate telemedicine in the future.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.