Early detection and surveillance of infant motor deficits: a wearable sensor toolkit for automated home evaluations

The National Institute of Child Health and Human Development awarded the Rehabilitation Institute of Chicago, doing business as Shirley Ryan AbilityLab, $720,463 on July 3, 2026, under the Child Health and Human Development Extramural Research program (CFDA 93.865) to develop a fully automated wearable sensor toolkit for early detection and surveillance of infant motor deficits.

The toolkit will detect motor deficits in infants within the first few months of life during free-living activities in home and community settings, addressing a gap in current evaluation methods that rely on caregiver reports and clinical observation lacking accessibility, objectivity, and precision. Under Aim 1, the recipient will record whole-body inertial sensor data from up to 60 newborn infants across the risk spectrum to curate a pediatric dataset of real-world early motor development. Aim 2 applies signal processing and machine learning techniques to develop a toolkit that automatically analyzes wearable sensor data to evaluate motor development for infants less than three months of age during everyday activities. Aim 3 validates the toolkit. The award addresses the fact that more than 17 percent of infants born in the United States have motor deficits, and that current detection often occurs at 12 to 24 months of age or later, reducing intervention efficacy due to late initiation and diminished brain plasticity.

The period of performance runs from July 3, 2026, through April 30, 2031. Work is performed in Chicago, Illinois.

Abstract

More than 17% of infants born in the united states have motor deficits which restrict their development, daily function, and quality of life. Early intervention can improve outcomes for these infants by effectively retraining the brain and body during a critical period of development. However, motor deficits are often not detected until 1-2 years of age or later, which can reduce the efficacy of interventions due to late initiation and lowered brain plasticity. Current evaluation methods, such as caregiver reports and observation-based clinical assessments, lack the accessibility, objectivity, and precision needed to evaluate early infant movement outside of a clinical setting and screen for atypical motor behaviors. The objective of the proposed study is to develop a fully automated wearable sensor toolkit to detect infant motor deficits within the first few months of life during free- living activities the home and community, and to surveil these deficits over the first year of life. Capturing free- living infant movements would provide a more comprehensive and representative picture of everyday infant behavior compared to current evaluation methods. We will develop the wearable sensor toolkit via the following aims. In aim 1, whole-body inertial sensor data will be recorded in the home and community from up to 60 newborn infants across the risk spectrum for motor deficits to curate a pediatric dataset of real-world, early motor development. In aim 2, this dataset will be used along with state-of-the-art signal processing and machine learning techniques to develop a toolkit that collects and automatically analyzes wearable sensor data to evaluate motor development for infants less than 3 months of age during everyday activities and infant behaviors in the home and community. Finally, in aim 3, the toolkit will be validated in a separate cohort of 30 newborn infants to evaluate its effectiveness in detecting and describing infant motor deficits over the first year of life compared to traditional clinical assessments. The primary outcomes will be a fully annotated, free-living dataset characterizing infant motor development between birth and 3 months of age, as well as the sensitivity and specificity of the wearable sensor toolkit to detect infant motor deficits during these early ages. Secondary outcomes will include surveillance curves of sensor-based metrics to track motor development over the first year of life, and clinical outcomes of infants tracked and not tracked by the toolkit. This study will revolutionize detection and surveillance of infant motor development and deficits by automatically evaluating early-life movement patterns in the home and community, alerting clinicians and caregivers when further examination is needed. Early, accurate, and accessible methods of identifying infant motor deficits would allow for more widespread developmental screening and earlier, targeted interventions to improve lifelong outcomes.