ArticleScientific reports2021
Novel AI driven approach to classify infant motor functions.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 76 citations in OpenAlex.
- Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk.JAMA network open · 2022Trial
- Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.Scientific reports · 2022Trial
- Markerless Motion Capture for Human Movement Estimation Using Artificial Intelligence: A Systematic Review.Pediatric reports · 2026Review
- The future of follow-up: technology-enabled medical and developmental follow-up care for NICU graduates.Journal of perinatology : official journal of the California Perinatal Association · 2026Review
- Video and Wearable Sensor Technologies for Early Detection of Cerebral Palsy in Infants: A Scoping Review.Journal of clinical medicine · 2026Review
- Infant limb kinematics and muscle force variation during kicking is associated with clinical cerebral palsy risk measures.PloS one · 2026Article
- Automatic infant 2D pose estimation from videos: Comparing seven deep neural network methods.Behavior research methods · 2025Article
- Towards universal early screening for cerebral palsy: a roadmap for automated General Movements Assessment.EClinicalMedicine · 2025Review
- Autonomous screening of infants at high risk for neurodevelopmental impairments using a radar sensor and machine learning.Scientific reports · 2025Article
- Trade-Offs Between Simplifying Inertial Measurement Unit-Based Movement Recordings and the Attainability of Different Levels of Analyses: Systematic Assessment of Method Variations.JMIR mHealth and uHealth · 2025Article
- Comparison of marker-less 2D image-based methods for infant pose estimation.Scientific reports · 2025Article
- Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants.Children (Basel, Switzerland) · 2025Article
- Can AI-Based Video Analysis Help Evaluate the Performance of the Items in the Bayley Scales of Infant Development?Children (Basel, Switzerland) · 2025Article
- Deep learning empowered sensor fusion boosts infant movement classification.Communications medicine · 2025Article
- Marker-Less Video Analysis of Infant Movements for Early Identification of Neurodevelopmental Disorders.Diagnostics (Basel, Switzerland) · 2025Article
- Quantifying spontaneous infant movements using state-space models.Scientific reports · 2024Article
- Towards novel classification of infants' movement patterns supported by computerized video analysis.Journal of neuroengineering and rehabilitation · 2024Article
- Artificial intelligence detects awareness of functional relation with the environment in 3 month old babies.Scientific reports · 2024Article
- The General Movement Optimality Score-Revised (GMOS-R) with Socioeconomically Stratified Percentile Ranks.Journal of clinical medicine · 2024Article
- Reduction in limb-movement complexity at term-equivalent age is associated with motor developmental delay in very-preterm or very-low-birth-weight infants.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
11 authors at 7 institutions in 4 countries.
Funding
Abstract
The past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessment (GMA). This study proposes a novel machine learning algorithm to detect an age-specific movement pattern, the fidgety movements (FMs), in a prospectively collected sample of typically developing infants. Participants were recorded using a passive, single camera RGB video stream. The dataset of 2800 five-second snippets was annotated by two well-trained and experienced GMA assessors, with excellent inter- and intra-rater reliabilities. Using OpenPose, the infant full pose was recovered from the video stream in the form of a 25-points skeleton. This skeleton was used as input vector for a shallow multilayer neural network (SMNN). An ablation study was performed to justify the network's architecture and hyperparameters. We show for the first time that the SMNN is sufficient to discriminate fidgety from non-fidgety movements in a sample of age-specific typical movements with a classification accuracy of 88%. The computer-based solutions will complement original GMA to consistently perform accurate and efficient screening and diagnosis that may become universally accessible in daily clinical practice in the future.
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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.