# Quantifying patient-specific changes in neuromuscular control in cerebral palsy

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $71,735

## Abstract

Project Summary
The processes by which people with cerebral palsy (CP) adapt and learn new movement patterns are poorly
understood. The Parent R01 for this NINDS Research Supplement to Promote Diversity in Health-Related
Research seeks to quantify and understand motor adaptation in CP. Specifically, how individual differences in
adaptation influence responses to rehabilitation that aims to improve walking function. Multimodal feedback
training is a promising approach to support adaptation and motor learning, but few studies have evaluated
adaptation with real-time feedback training during walking or its impacts on walking function. The Parent R01
seeks to fill these gaps by completing the systematic experimental analyses necessary to quantify walking
adaption rates (Aim-1) and determine whether repeated exposure to multimodal feedback training can alter
adaptation rates (Aim-2) to induce motor learning and improve walking function (Aim-3). We provide
multimodal feedback using (1) sensorimotor feedback from adaptive ankle resistance delivered via a light-
weight, wearable robot and (2) audiovisual feedback of plantar flexor activity from EMG recordings. To
complement and extend this work, this Research Supplement will evaluate novel computational approaches
bridging machine learning and causal modeling to evaluate individual responses to feedback training. The
trainee will develop expertise bridging engineering, data science, and rehabilitation to (Supplement Aim-1)
develop individual models of response to feedback training that can be used to track progression within and
across sessions and (Supplement Aim-2) expand access to biomechanical monitoring and feedback training
outside of research laboratories. Supplement Aim-1 uses Bayesian Additive Regression Trees to leverage the
step-by-step data captured during the Parent R01 12 sessions of treadmill training to quantify changes in gait.
Supplement Aim-2 tests the accuracy of Shallow Recurrent Decoder Networks to reconstruct key
biomechanical variables from a sparse set of wearable sensors. This research will be complemented by
extensive training in data science, rehabilitation, and biomechanics by a multidisciplinary team of mentors to
support the trainee's future goals to pursue a tenure-track faculty position in engineering. Together, this
research will introduce new methods to understand and optimize rehabilitation to individual needs and
priorities.

## Key facts

- **NIH application ID:** 10986843
- **Project number:** 3R01NS091056-08S1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Michael Hart Schwartz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $71,735
- **Award type:** 3
- **Project period:** 2015-09-30 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10986843

## Citation

> US National Institutes of Health, RePORTER application 10986843, Quantifying patient-specific changes in neuromuscular control in cerebral palsy (3R01NS091056-08S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10986843. Licensed CC0.

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