SynthesisSensors (Basel, Switzerland)2023
Objective Measurement of Posture and Movement in Young Children Using Wearable Sensors and Customised Mathematical Approaches: A Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Machine Learning Applications for In-School Physical Activity Data Using IMUs in Children and Adolescents: A Systematic Review for Health Promotion.Journal of primary care & community healthPooled it
- Body position classification using wearable sensors in infants with cerebral palsy.Infant behavior & development · 2026Article
- Can slow and (un)steady still win the race? A narrative review of developmental gait characteristics and clinical implications in children born preterm.European journal of pediatrics · 2026Review
- Thinking Critically About Algorithms for Automated Detection of Behavior: 11 Guidelines for Social and Behavioral Scientists.Developmental science · 2026Article
- Digital Pedoscopic Assessment and Data-Driven Classification of Pediatric Rearfoot Alignment.Children (Basel, Switzerland) · 2025Article
- Machine Learning Methods in Posture-Related Applications in Children up to 12 Years Old: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- Visualising accelerometer-based 24/7 human movement behaviour data: an umbrella review and framework development from the LABDA project.Journal of activity, sedentary and sleep behaviors · 2025Review
- Monitoring of child-specific activities in ambulatory children with and without developmental disabilities.BMC pediatrics · 2025Article
- Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?Sensors (Basel, Switzerland) · 2024Article
- Psychometrics of Wearable Devices Measuring Physical Activity in Ambulant Children With Gait Abnormalities: A Systematic Review and Meta-analysis.Archives of rehabilitation research and clinical translation · 2024Review
- Evaluation of the ActiMotus Software to Accurately Classify Postures and Movements in Children Aged 3-14.Sensors (Basel, Switzerland) · 2024Article
- Preliminary Technical Validation of LittleBeats™: A Multimodal Sensing Platform to Capture Cardiac Physiology, Motion, and Vocalizations.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Given the importance of young children's postures and movements to health and development, robust objective measures are required to provide high-quality evidence. This study aimed to systematically review the available evidence for objective measurement of young (0-5 years) children's posture and movement using machine learning and other algorithm methods on accelerometer data. From 1663 papers, a total of 20 papers reporting on 18 studies met the inclusion criteria. Papers were quality-assessed and data extracted and synthesised on sample, postures and movements identified, sensors used, model development, and accuracy. A common limitation of studies was a poor description of their sample data, yet over half scored adequate/good on their overall study design quality assessment. There was great diversity in all aspects examined, with evidence of increasing sophistication in approaches used over time. Model accuracy varied greatly, but for a range of postures and movements, models developed on a reasonable-sized (n > 25) sample were able to achieve an accuracy of >80%. Issues related to model development are discussed and implications for future research outlined. The current evidence suggests the rapidly developing field of machine learning has clear potential to enable the collection of high-quality evidence on the postures and movements of young children.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.