# Functional data analytics for kinematic assessments of motor control

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $350,874

## Abstract

Project Summary.
 Stroke is the leading cause of long-term disability in the United States, with an incidence of roughly 800,000
events each year. With the annual incidence of stroke expected to surpass 1 million U.S. adults by 2025, there
will be escalating stroke-related disability and economic burdens over the next decade. Thus, there is a great
clinical and economic need to develop targeted neurorehabilitative strategies that are based on better charac-
terization of the motor impairment after stroke using outcomes that are detailed, objective and quantifiable.
 Kinematic data are densely-sampled recordings of entire movements, and provide exactly the kind of data
that are needed for detailed assessments motor control following stroke. While these data are commonly col-
lected, typical analyses do not take advantage of their inherent richness; instead, these analyses focus on a
small number of derived summaries such as including endpoint bias and variability
 Our proposal develops functional data approaches for kinematic data motivated by three existing datasets.
Our first aims introduce statistically novel method for functional response models by incorporating covariates
into registration, mean regression and covariance modeling. We combine these analysis components, which
are typically treated as distinct problems, to assess the relationship between motion speed and quality, and
introduce novel techniques for variable selection. Our second aims use neuroimaging to provide insights into
the anatomical processes underlying motor control impairments, and consider stroke location, structural con-
nections, and increased cortical activity as drivers of impairment and recovery. Throughout, we use a Bayesian
approach that jointly models all parameters of interest. All new methods will be implemented in robust, publicly
available software, be validated on simulated datasets designed to mimic real-data scenarios, and be deployed
on the motivating datasets to generate insights into the mechanisms behind motor control impairment following
stroke.

## Key facts

- **NIH application ID:** 9934306
- **Project number:** 5R01NS097423-05
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Jeff Goldsmith
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $350,874
- **Award type:** 5
- **Project period:** 2016-08-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9934306

## Citation

> US National Institutes of Health, RePORTER application 9934306, Functional data analytics for kinematic assessments of motor control (5R01NS097423-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9934306. Licensed CC0.

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