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Updated: Oct 16, 2025
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
Published on: May 17, 2024
Miniaturized wireless, skin-integrated sensor networks for quantifying full-body movement behaviors and vital signs
Hyoyoung Jeong1, Sung Soo Kwak1, Seokwoo Sohn2
1Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208.
Insights
This study presents a low-cost, wearable sensor system for early detection of infant neuromotor disorders. The technology quantitatively tracks infant movements and vital signs, enabling remote assessment and timely intervention.
Area of Science:
- Biomedical Engineering
- Pediatric Neurology
- Rehabilitation Technology
Background:
- Early identification of atypical infant movement is crucial for timely intervention in neuromotor pathologies.
- Traditional neuromotor assessments are costly, qualitative, and require specialized personnel and facilities.
- Current methods limit accessibility and quantitative insights into infant motor development.
Purpose of the Study:
- To introduce a novel, low-cost technology for quantitative, continuous, full-body kinematic and vital signs monitoring of infants.
- To provide a system for remote assessment of infant motor skills, facilitating early detection of developmental issues.
- To explore the potential of this technology in automating and systematizing traditional motor assessments.
Main Methods:
- Development of a wireless network of small, flexible inertial sensors for synchronized, wide-bandwidth data capture.
- Placement of sensors on infants for continuous monitoring during free-living conditions.
- Reconstruction of 3D infant motion in avatar form and simultaneous recording of vital signs (temperature, heart rate, respiratory rate).
Main Results:
- Demonstrated successful quantitative and semi-quantitative assessment of gross motor skills in infants at low and elevated risk.
- Collected long-term, follow-up data over a 3-month period post-birth.
- Validated the system's capability for remote visual assessment and machine learning analysis.
Conclusions:
- The developed system offers a pragmatic, low-cost alternative for early detection of atypical infant neuromotor behaviors.
- This technology has the potential for scaled deployment to improve global child health outcomes through early detection.
- Quantitative kinematic data combined with vital signs can enhance the assessment of infant development and facilitate early therapeutic interventions.
Abstract:
Early identification of atypical infant movement behaviors consistent with underlying neuromotor pathologies can expedite timely enrollment in therapeutic interventions that exploit inherent neuroplasticity to promote recovery. Traditional neuromotor assessments rely on qualitative evaluations performed by specially trained personnel, mostly available in tertiary medical centers or specialized facilities. Such approaches are high in cost, require geographic proximity to advanced healthcare resources, and yield mostly qualitative insight. This paper introduces a simple, low-cost alternative in the form of a technology customized for quantitatively capturing continuous, full-body kinematics of infants during free living conditions at home or in clinical settings while simultaneously recording essential vital signs data. The system consists of a wireless network of small, flexible inertial sensors placed at strategic locations across the body and operated in a wide-bandwidth and time-synchronized fashion. The data serve as the basis for reconstructing three-dimensional motions in avatar form without the need for video recordings and associated privacy concerns, for remote visual assessments by experts. These quantitative measurements can also be presented in graphical format and analyzed with machine-learning techniques, with potential to automate and systematize traditional motor assessments. Clinical implementations with infants at low and at elevated risks for atypical neuromotor development illustrates application of this system in quantitative and semiquantitative assessments of patterns of gross motor skills, along with body temperature, heart rate, and respiratory rate, from long-term and follow-up measurements over a 3-mo period following birth. The engineering aspects are compatible for scaled deployment, with the potential to improve health outcomes for children worldwide via early, pragmatic detection methods.
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