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Multi-Modal Home Sleep Monitoring in Older Adults
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Longitudinal Infant Sleep Monitoring Using a Sensor-Enabled Responsive Bassinet: A Population-Scale Feasibility

Savannah Gluck1, Teresa A Lillis2, Karthik Aroor3

  • 1Mrs. T.H. Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 90089, USA.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
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This study shows how smart bassinets can track infant sleep patterns from birth to six months. The technology provides new insights into infant fussing and caregiver responses, improving sleep monitoring.

Area of Science:

  • Infant Sleep Science
  • Biomedical Engineering
  • Data Science

Background:

  • Sleep disturbances in infants are linked to negative outcomes for both infants and caregivers.
  • Limited data exists on the longitudinal sleep development of infants from birth to six months.
  • Traditional infant sleep monitoring methods have significant limitations.

Purpose of the Study:

  • To assess the feasibility of using Internet of Things (IoT) bassinet data for infant sleep monitoring.
  • To derive traditional and novel sleep metrics from aggregated activity logs.
  • To characterize infant fussing and the impact of responsive bassinet features.

Main Methods:

  • Utilized aggregated activity logs from a commercial IoT bassinet.
  • Analyzed data from 26,187 infants.
Keywords:
IoT bassinetcryinginfantlongitudinal data collectionsleep

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Last Updated: Jul 16, 2026

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  • Employed a data-driven approach to define sleep/fussing periods and select appropriate nights for analysis.

    • Main Results:

    • Successfully derived traditional sleep metrics (longest sleep stretch, total night sleep, sleep efficiency).
    • Identified novel metrics for infant fussing and caregiver intervention delays.
    • Demonstrated unprecedented resolution in longitudinal infant sleep data.

    Conclusions:

    • IoT bassinet technology offers a feasible method for detailed infant sleep and fussing monitoring.
    • This approach provides a framework for descriptive and comparative sleep analysis.
    • Sensor-enabled responsive technology can capture dyadic interactions not measured by traditional methods.