# Learning invariant representation from high- dimensional data for quantitative stroke reha

> **NIH NIH R01** · NEW YORK UNIVERSITY · 2022 · $299,999

## Abstract

Advances in wearable electronics, personal mobile devices, and sensor technology are opening the door to
many promising applications in medical care and biomedical research. However, the resulting datasets are
often challenging to process due to variability caused by extraneous effects unrelated to the tasks of
interest, such as changes in environmental conditions, heteroscedasticity in measurement noise, or patient
idiosyncrasies. These effects produce systematic differences between the data used to train machine-
learning algorithms and the data on which they are applied in practice, impairing real-world performance.
The proposed research will address the fundamental problem of factoring out extraneous effects
associated with known nuisance variables. We will develop a novel methodology for extracting features that
ar.e invariant to nuisance variables-and hence also to the associated extraneous effects-but that are still
useful for classification or regression. The methodology is based on nonparametric deep-network models
that perform automatic normalization of the data, and further enforce invariance via adversarial learning.
We will apply the approach to an important problem in stroke rehabilitation, the quantitated dosing of motor
training. Using a dataset of sensor-based motion data, we will train the model to identify and count
functional movements in stroke patients performing rehabilitation activities. We expect to show that our
approach can surmount patient variability to enable rigorous movement classification and quantitation. The
proposed work is significant, because it will empower investigators to undertake the dosing trials critically
needed in stroke rehabilitation. The proposed work is innovative, because it departs from traditional data
preprocessing techniques by combining advanced data normalization and model calibration procedures.
Our work is likely to have a positive impact on stroke rehabilitation by facilitating the research required to
change clinical practice and improve stroke outcomes. Our quantitative approach is broadly generalizable
to applications hindered by nuisance variables, such as medical diagnostics and genomics.

## Key facts

- **NIH application ID:** 10469389
- **Project number:** 5R01LM013316-04
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Carlos Fernandez-Granda
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $299,999
- **Award type:** 5
- **Project period:** 2019-07-16 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10469389

## Citation

> US National Institutes of Health, RePORTER application 10469389, Learning invariant representation from high- dimensional data for quantitative stroke reha (5R01LM013316-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10469389. Licensed CC0.

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