# Quantitative Rehabilitation after Stroke

> **NIH NIH K02** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $224,790

## Abstract

PROJECT SUMMARY
Stroke causes significant disability, and recovery is often incomplete. In animal models of stroke, robust upper
extremity (UE) motor recovery can be elicited if high doses of functional training are given early. In humans,
however, the optimal training dose is unknown, because no quantitative dose-response trials have been
undertaken in the first months after stroke. This deficiency stems from a lack of measurement instruments that
can accurately and easily quantify UE functional training dose and recovery. To address this gap, this proposal
will generate two new measurement tools to enable quantitative stroke recovery research. The first tool (Aim 1)
will quantify the number of functional movements made during stroke rehabilitation, measuring UE training
dose. The second tool (Aim 2) will quantify the abnormality of movements, measuring UE recovery and
response to interventions. We will combine wireless motion capture and computational methodologies to
create objective, precise, and user-friendly tools. Inertial measurement units, worn by individuals with stroke
and healthy controls performing various activities, will capture upper body motion. In Aim 1, machine learning
algorithms will be trained to identify and count functional movements in activities normally practiced during
rehabilitation. In Aim 2, functional principal components analysis will quantify movement impairment and
compensation in standardized motions. Validity will be determined by correlating tool outcomes with current
gold standards. The proposed study will be conducted at New York University, in collaboration with
investigators from the NYU Center for Data Science, Columbia University, and Washington University-St.
Louis. Each have complementary expertise in machine learning, functional data analysis, and functional
movement identification. This K02 Independent Scientist Award will provide the candidate with skill in
advanced motion capture and analytical methodologies needed to study stroke rehabilitation and recovery. The
career development plan includes personalized tutorials and coursework combined with longitudinal oversight
of data analysis, providing an excellent foundation for launching an independent research career. Ultimately,
the developed tools have the potential to immediately impact neurorehabilitation research, facilitating the
rigorous dose-response trials so critically needed to change clinical practice and improve stroke outcomes.

## Key facts

- **NIH application ID:** 9973097
- **Project number:** 5K02NS104207-04
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Heidi Schambra
- **Activity code:** K02 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $224,790
- **Award type:** 5
- **Project period:** 2017-09-30 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9973097

## Citation

> US National Institutes of Health, RePORTER application 9973097, Quantitative Rehabilitation after Stroke (5K02NS104207-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9973097. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*