ArticleSensors (Basel, Switzerland)2020
Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 5 of them syntheses 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
42 citing papers in PubMed, 5 syntheses or guidelines pooled it, 100 citations in OpenAlex.
- Association between 24-hour movement behaviors and adiposity in children and adolescents: A compositional data meta-analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Pooled it
- Objective Measurement of Posture and Movement in Young Children Using Wearable Sensors and Customised Mathematical Approaches: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Adherence to the World Health Organization's physical activity recommendation in preschool-aged children: a systematic review and meta-analysis of accelerometer studies.The international journal of behavioral nutrition and physical activity · 2023Pooled it
- Systematic review of accelerometer-based methods for 24-h physical behavior assessment in young children (0-5 years old).The international journal of behavioral nutrition and physical activity · 2022Pooled it
- 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
- Understanding Multilevel Correlates of Long-Term Physical Activity Trajectories Among Middle-Aged and Older Adults: A Machine-Learning Analysis.Behavioral sciences (Basel, Switzerland) · 2026Article
- Compositional movement behaviours and preschool children's social-emotional development.The international journal of behavioral nutrition and physical activity · 2026Article
- Exploring approaches to computational representation and classification of user-generated meal logs.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Predicting physical activity types in children aged 3-6 from video data using computer vision.BMC public health · 2026Article
- Machine and Deep Learning for Detection of Moderate-to-Vigorous Physical Activity From Accelerometer Data: Systematic Scoping Review.Interactive journal of medical research · 2026Review
- Validation and optimisation of wearable accelerometer data pre-processing for digital measure implementation and development.PloS one · 2026Article
- Article
- Profile of the PLAY spaces & environments for children's physical activity, sedentary behaviour and sleep (PLAYCE) cohort study, Western Australia.Journal of activity, sedentary and sleep behaviors · 2025Article
- Machine learning modeling for predicting adherence to physical activity guideline.Scientific reports · 2025Article
- Validity and reliability of the movement behaviour questionnaire child in Chinese preschoolers.Frontiers in pediatrics · 2025Article
- Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?Sensors (Basel, Switzerland) · 2024Article
- Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Developmental trends in young children's device-measured physical activity and sedentary behaviour.The international journal of behavioral nutrition and physical activity · 2024Article
- Reliability and validity of rapid assessment tools for measuring 24-hour movement behaviours in children aged 0-5 years: the Movement Behaviour Questionnaire Baby (MBQ-B) and child (MBQ-C).The international journal of behavioral nutrition and physical activity · 2024Article
- An automated, data-driven approach to children's social dynamics in space and time.Child development perspectives · 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
3 authors at 1 institution in 1 country.
Funding
Abstract
Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows comprised of multiple activity types by using shorter windows, including temporal features such as standard deviation in lag and lead windows, and using multiple sensors may improve the classification accuracy under free-living conditions. The objective of this study was to evaluate the accuracy of Random Forest (RF) activity classification models for preschool-aged children trained on free-living accelerometer data. Thirty-one children (mean age = 4.0 ± 0.9 years) completed a 20 min free-play session while wearing an accelerometer on their right hip and non-dominant wrist. Video-based direct observation was used to categorize the children's movement behaviors into five activity classes. The models were trained using prediction windows of 1, 5, 10, and 15 s, with and without temporal features. The models were evaluated using leave-one-subject-out-cross-validation. The F-scores improved as the window size increased from 1 to 15 s (62.6%-86.4%), with only minimal improvements beyond the 10 s windows. The inclusion of temporal features increased the accuracy, mainly for the wrist classification models, by an average of 6.2 percentage points. The hip and combined hip and wrist classification models provided comparable accuracy; however, both the models outperformed the models trained on wrist data by 7.9 to 8.2 percentage points. RF activity classification models trained with free-living accelerometer data provide accurate recognition of young children's movement behaviors under real-world conditions.
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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.