Evidence map›Paper›PMID 41978058›Full record

ArticleSensors (Basel, Switzerland)2026

A Novel Workflow to Estimate Limb Orientation from Wearable Sensors to Monitor Infant Motor Development.

David Song, William J Kaiser, Sitaram Vangala, Rujuta B Wilson

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

David SongDepartment of Psychiatry, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0003-2526-7521
William J KaiserDepartment of Electrical Engineering, University of California, Los Angeles, CA 90095, USA.
Sitaram VangalaDepartment of Medicine Statistics Core, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA.
Rujuta B WilsonDepartment of Psychiatry, University of California, Los Angeles, CA 90095, USA.

Funding

National Institutes of Child Health and Human Development K23HD099275Simons Foundation 977910UCLA Intellectual and Developmental Disabilities Research Center 1P50HD103557UCLA Tarjan Center for Excellence in Developmental Disabilities 90DDUC0129
6 · The paper itself

Abstract

backgroundWearable sensors have gained increasing popularity as an objective method for remotely monitoring infant movement in naturalistic settings. Over the first year of life, infants generate a wide range of motions, from goal-directed to spontaneous movement. These include linear movements, such as kicks, and orientation changes, such as postural transitions. Many sensor processing pipelines emphasize capturing linear movements through movement-generated acceleration while focusing less on information about orientation embedded in the gravitational part of the data. Here, we introduce a complementary gravity-referenced approach that extracts the gravitational component of accelerometer signals to estimate limb orientation, extending the reliable quantification of rich and detailed aspects of infant movement. Infant orientation has demonstrated clinical relevance, including associations with later neuromotor outcomes, and it can be used to chart infant motor development, motivating the development of objective methods to quantify orientation from sensor data.

methodsWearable sensors (Opal APDM) were used to longitudinally evaluate infant motor activity recorded in sessions conducted at 3, 6, 9, and 12 months of age. We extracted data from a 5 min segment that has simultaneous video recordings. From these datasets, applying the gravity-referenced method, we computed pitch, roll, and yaw, angles that collectively describe limb orientation. We then quantified orientation variability using axis-specific circular standard deviations (SDs) for pitch, roll, and yaw and a multi-axis composite measure based on generalized variance.

resultsAxis-specific circular SDs for pitch, roll, and yaw, as well as the composite generalized variance, increased significantly from 3 to 12 months (

conclusionsOverall, gravity-referenced pitch, roll, and yaw provide rich orientation features that increased as infants develop more postural transitions. Furthermore, the orientation features correlated with standardized measures of infant motor function. These orientation metrics can complement traditional linear kinematic measures and improve our ability to granularly track infant motor development in the first year of life.

Indexed as

Child DevelopmentExtremitiesWearable Electronic DevicesAccelerometryFemaleHumansInfantMaleMonitoring, PhysiologicMovementPostureinfantmotor developmentorientationwearable sensors

Identifiers

PMID41978058
PMCPMC13075267

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.