ArticleScientific reports2020
Markerless Measurement and Evaluation of General Movements in Infants.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 58 citations in OpenAlex.
- Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.Scientific reports · 2022Trial
- Nonlinear Measures Applied to Spontaneous Infant Movement Analysis: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Video and Wearable Sensor Technologies for Early Detection of Cerebral Palsy in Infants: A Scoping Review.Journal of clinical medicine · 2026Review
- Movement smoothness at term-equivalent age is associated with motor developmental delay in preterm infants.Scientific reports · 2025Article
- Automatic infant 2D pose estimation from videos: Comparing seven deep neural network methods.Behavior research methods · 2025Article
- Autonomous screening of infants at high risk for neurodevelopmental impairments using a radar sensor and machine learning.Scientific reports · 2025Article
- Comparison of marker-less 2D image-based methods for infant pose estimation.Scientific reports · 2025Article
- A systematic review of portable technologies for the early assessment of motor development in infants.NPJ digital medicine · 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
- Risk of autism spectrum disorder at 18 months of age is associated with prenatal level of polychlorinated biphenyls exposure in a Japanese birth cohort.Scientific reports · 2024Article
- Artificial intelligence detects awareness of functional relation with the environment in 3 month old babies.Scientific reports · 2024Article
- Spatiotemporal patterns of spontaneous movement in neonates are significantly linked to risk of autism spectrum disorders at 18 months old.Scientific reports · 2023Article
- Infant movement classification through pressure distribution analysis.Communications medicine · 2023Article
- Mobile Solutions for Clinical Surveillance and Evaluation in Infancy-General Movement Apps.Journal of clinical medicine · 2023Review
- An Algorithm for the Detection of General Movements of Preterm Infants Based on the Instantaneous Heart Rate.Children (Basel, Switzerland) · 2022Article
- Prediction of autistic tendencies at 18 months of age via markerless video analysis of spontaneous body movements in 4-month-old infants.Scientific reports · 2022Article
- Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions.Sensors (Basel, Switzerland) · 2022Review
- Article
- Novel AI driven approach to classify infant motor functions.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
General movements (GMs), a type of spontaneous movement, have been used for the early diagnosis of infant disorders. In clinical practice, GMs are visually assessed by qualified licensees; however, this presents a difficulty in terms of quantitative evaluation. Various measurement systems for the quantitative evaluation of GMs track target markers attached to infants; however, these markers may disturb infants' spontaneous movements. This paper proposes a markerless movement measurement and evaluation system for GMs in infants. The proposed system calculates 25 indices related to GMs, including the magnitude and rhythm of movements, by video analysis, that is, by calculating background subtractions and frame differences. Movement classification is performed based on the clinical definition of GMs by using an artificial neural network with a stochastic structure. This supports the assessment of GMs and early diagnoses of disabilities in infants. In a series of experiments, the proposed system is applied to movement evaluation and classification in full-term infants and low-birth-weight infants. The experimental results confirm that the average agreement between four GMs classified by the proposed system and those identified by a licensee reaches up to 83.1 ± 1.84%. In addition, the classification accuracy of normal and abnormal movements reaches 90.2 ± 0.94%.
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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.