# A Data Science Approach To Personalizing Sensorimotor Training Post-Stroke

> **NIH NIH R56** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2022 · $716,695

## Abstract

Project Summary
The large variability in lesions, impairment, and responsiveness to therapy following stroke has hindered the
development of principled and effective approaches to neurorehabilitation of the upper extremity (UE). Here,
building on our multidisciplinary expertise in large-scale neurorehabilitation studies, brain imaging, and data
science, as well as our established collaborations, we propose a novel self-improving personalized rehabilitation
system based on a continuous loop between data, algorithm, and treatment. In Aim 1, we will create and
simulate a self-improving algorithm for personalized UE rehabilitation, based on the state-of-the-art and
theoretically-sound “Bayesian contextual bandits” algorithm. A predictive dynamics model will generate time-
varying predictions of the UE functional outcome (the ARAT) at 3 months post-stroke in response to training and
based on the patient baseline clinical and neural variables that modulate the effects of treatment, and on prior
knowledge from similar patients. Second, a sequential-decision bandit algorithm will use the model’s predictions
for different (simulated) doses to select the weekly dose that is predicted to maximize the ARAT at 3 months
post-stroke, given constraints on the feasible doses. In Aim 2, we will conduct a cohort study of 400
individuals with sub-acute stroke. The detailed treatment, neural, and clinical data generated by this study
will allow us to identify the clinical and neural modulators of sensorimotor training as well as the feasible doses
in sub-acute stroke. In particular, treatment data will include the schedule of arm movements recorded via an
exoskeleton during mechanized adjuvant therapy and the number of hand movements both during and outside
conventional therapy recorded with a wrist-worn sensor. In Aim 3, we will perform a proof-of-concept study
of the self-improving precision neurorehabilitation system, with 150 participants in the sub-acute phase
post-stroke. The algorithm of Aim 1 will determine the optimal weekly doses based on previous measurements
of the actual doses, the measured ARAT, and the participant clinical and neural context. This Aim will start in
year 3 after the data from 200 participants in Aim 2 have been collected to update the algorithm of Aim 1. The
data generated by this Aim will continuously be used to update the model of Aim 1, yielding better predictions
and thus more effective schedules of weekly doses. We will therefore test the hypothesis that the change in
ARAT from baseline to 3-month will be significantly greater for each new group of 50 participants compared to
the previous group. Upon completion of this research, we will have captured a comprehensive picture of
rehabilitation treatment parameters that most improve UE function given the heterogeneous profiles after stroke.
The resulting unique database of 550 participants, which will be made publicly available, is expected to become
a reference in neurorehabilitation...

## Key facts

- **NIH application ID:** 10592727
- **Project number:** 1R56NS126748-01
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Olivier Remy-Neris
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $716,695
- **Award type:** 1
- **Project period:** 2022-06-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10592727

## Citation

> US National Institutes of Health, RePORTER application 10592727, A Data Science Approach To Personalizing Sensorimotor Training Post-Stroke (1R56NS126748-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10592727. Licensed CC0.

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