# Automated Assessment of Infant Sleep/Wake States, Physical Activity, and Household Noise Using a Multimodal Wearable Device and Deep Learning Models

> **NIH NIH R01** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2024 · $606,042

## Abstract

Sleep and physical activity/sedentary behavior are physiological and behavioral processes that are intricately
intertwined. Their Interconnectedness is particularly pronounced during early infancy when these systems are
rapidly developing in concert with neurobiological changes. Yet, sleep and physical activity/sedentary behavior
are often studied in isolation and with little attention to the home environment in which they occur. Further, current
state-of-the-art methods, including wearables and mobile sensing devices that automate assessment of sleep,
physical activity, and sedentary behavior, have been developed and validated predominantly with adults,
adolescents, and school-age children. These adult-based methods, however, do not translate to infant
populations and the unique challenges posed by this development period. With these issues in mind, our
overarching aim is to advance methodological tools that provide valid, automated, objective, and fine-grained
assessments of infant health behaviors in real-world environments. In doing so, we will use LittleBeats, an infant
multimodal wearable device engineered by our team, that integrates a microphone to collect audio data, a 3-
lead electrocardiogram (ECG) to assess infant cardiac physiology, and an inertial measurement unit (IMU)
sensor to assess infant motion and posture. LittleBeats can be worn by infants for extended periods of time (8-
10 hours) in their natural environments without researchers present. We will leverage high-density data from this
infant wearable to address three specific aims. First, we will develop and validate multimodal deep learning (DL)
algorithms that use audio, ECG, and motion data as input to detect infant sleep/wake states, including quiet
sleep, active sleep, drowsy, quiet alert, active alert, and crying states. Second, we will develop and validate DL
algorithms that use ECG and motion data as input to detect infant physical activity (i.e., tummy time) and
sedentary time (e.g., time restrained in a car seat). Third, because environmental noise, including loudness,
variability, and number of sound sources have been associated with negative physiological, behavioral, and
cognitive outcomes during the first year of life, we will leverage audio data from the LittleBeats device worn by
the infant to detect noises in the home environment. Our development and validation will occur across two
samples of infants under six months of age. DL algorithms will be validated against (a) annotations by trained
and certified human coders, (b) ecological momentary assessments provided by infants’ primary caregivers, and
(c) polysomnography, the gold-standard for sleep. By bringing together assessments of infant sleep, physical
activity, sedentary behavior, and household noise under a single platform, we aim to advance research capacity
to investigate the interdependencies and transactions among these core infant health behaviors and the
environments in which they occur. Ultimatel...

## Key facts

- **NIH application ID:** 10981817
- **Project number:** 1R01DK138866-01A1
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** MARK ALLAN HASEGAWA-JOHNSON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $606,042
- **Award type:** 1
- **Project period:** 2024-07-20 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10981817

## Citation

> US National Institutes of Health, RePORTER application 10981817, Automated Assessment of Infant Sleep/Wake States, Physical Activity, and Household Noise Using a Multimodal Wearable Device and Deep Learning Models (1R01DK138866-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10981817. Licensed CC0.

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