# Automated Assessment of Neurodevelopment in Infants at Risk for Motor Disability

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $670,354

## Abstract

PROJECT SUMMARY/ABSTRACT
The overall goal of this R01 project is to develop an automated assessment system that can capitalize on state of
the art sensing technologies and machine learning algorithms to enable accurate and early detection of infants
at risk for neurodevelopmental disabilities. In the USA, 1 in 10 infants are born at risk for these disabilities.
For children with neurodevelopmental disabilities, early treatment in the first year of life improves long-term
outcomes. However, we are currently held back by inadequacies of available clinical tests to measure and
predict impairment. Existing tests are hard to administer, require specialized training, and have limited long-
term predictive value. There is a critical need to develop an objective, accurate, easy-to-use tool for the early
prediction of long-term physical disability. The field of pediatrics and infant development would greatly benefit
from a quantitative score that would correlate with existing clinical measures used today to detect movement
impairments in very young infants. To realize a new generation of tests that will be easy to administer, we will
obtain large datasets of infants playing in an instrumented gym or simply being recorded while moving in a
supine posture. Video and sensor data analyses will convert movement into feature vectors based on our
knowledge of the problem domain. Our approach will use machine learning to relate these feature vectors to
currently recommended clinical tests or other ground truth information. The power of this design is that
algorithms can utilize many aspects of movement to produce the relevant scores. Our preliminary data allows
us to lay the following aims: 1)Aim 1: To assess concurrent validity of a multimodal instrumented
gym with existing clinical tools. Here, using 150 infants (75 with early brain injury and 75 controls), we
will focus on converting data from an instrumented gym into estimates of the standard clinical tests; 2)Aim 2:
To develop a computer vision-based algorithm to quantify infant motor performance from
single camera video. Here using video data from 1200 infants (400 with early brain injury, 400 preterm
without early brain injury, 400 controls), plus those gathered from Aim 1 and Aim 3, we will extract pose data
from single-camera video recordings and convert these into kinematic features and relevant scores needed to
classify infant movement; 3)Aim3: To discover the features related to long-term motor development.
Here we will convert data collected longitudinally from 50 infants (25 with early brain injury and 25 controls)
using both instrumented gym and video recordings into estimates standard clinical tests change over time and
track features over developmental timescales. These three aims spearhead the use of real world behavior for
movement scoring. Our aims will bring us closer to a universal non-invasive test for early detection of
neurodevelopmental disabilities and lay the groundwork for long-term p...

## Key facts

- **NIH application ID:** 9993993
- **Project number:** 5R01HD097686-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** MICHELLE J. JOHNSON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $670,354
- **Award type:** 5
- **Project period:** 2019-08-07 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9993993

## Citation

> US National Institutes of Health, RePORTER application 9993993, Automated Assessment of Neurodevelopment in Infants at Risk for Motor Disability (5R01HD097686-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9993993. Licensed CC0.

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