# Multimodal Guidance towards Precision Rehabilitation to Improve Upper Extremity Function in Stroke Patients

> **NIH VA I21** · VETERANS HEALTH ADMINISTRATION · 2024 · —

## Abstract

The lifetime risk of stroke is 1 in 6 with an estimated 33 million stroke survivors worldwide. Ideally acute
stroke patients would receive an accurate and rapid prognosis regarding return of motor function, followed
by application of those therapies most able to improve it. Yet decisions regarding post-acute treatment of
stroke patients are made on short-term assessments of function that may be influenced by concurrent
treatment, time-of-day, motivation, and other factors. Those assessments are often delayed, with resultant
delays in rehabilitation treatments. There are important decisions that need to be made about the setting
where rehabilitation occurs, if it is needed, and where the stroke patient will best live in the long-term. This
research project aims to significantly add to the current understanding of biomarkers that can be used to
provide better diagnosis, rehabilitative treatment, and long-term disposition advice for veterans who
experience upper-extremity impairments from stroke. The gaps in knowledge we aim to address are the
unknown relationships between 1. immediate post-stroke movement and functional ability, and 2. between
sympathetic tone and psychological response to disability. Clinicians do not yet know how to use the data
from wearable technologies that measure these factors – a problem caused by the volume of data generated
and lack of reliable biomarkers derived from it. Our central hypothesis is that application of machine
learning techniques to data from a multimodal sensor array worn by a patient for multiple hours can
provide better evidence of motor ability, assess latent psychological factors, and predict recovery trajectory
better than conventional short-term assessments. It may also allow more rapid personalization of therapy
plans based on real-world deficits discovered through sensor-based data. We will test our central hypothesis
by pursuing the two following specific aims with associated working hypotheses:
 1. Collect functionally relevant data from a wearable inertial, electromyographic, and
electrodermal sensor array. Working Hypothesis: A few strategically placed sensors can capture
functional movement and state of the autonomic nervous system. Kinematic and physiological measures
taken during task performance will be correlated with motor impairment and functional status. Completion
of this aim will lead to the identification of functional variables derived from multimodal sensor
measurements and demonstrate the feasibility of, and challenges to, inpatient use of a sensor array.
 2. Predict key clinical outcomes from sensor array-derived variables in acute stroke
inpatients being evaluated for post-discharge therapies. Working Hypothesis: Machine learning
techniques, including Bayesian fusion, will predict deficits and discharge disposition from the multimodal
variables collected. The electrodermal response to challenging movement is an unexplored area that may
provide insight into motivation and affecti...

## Key facts

- **NIH application ID:** 10754870
- **Project number:** 5I21RX004076-02
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** GEORGE F. WITTENBERG
- **Activity code:** I21 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2022-12-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10754870

## Citation

> US National Institutes of Health, RePORTER application 10754870, Multimodal Guidance towards Precision Rehabilitation to Improve Upper Extremity Function in Stroke Patients (5I21RX004076-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10754870. Licensed CC0.

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