ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2020
Computer Vision to Automatically Assess Infant Neuromotor Risk.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02813889 (SmarToyGym), which is not on this map. Cited by 45 papers, 1 of them a synthesis that pooled it.
What it found
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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.
SmarToyGym: Smart Detection of Atypical Toy-oriented Actions in At-risk Infants
Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation.Journal of neuroengineering and rehabilitation · 2023Pooled it
- Movement-Related Differences Between Infants with High and Low Likelihood of Autism: A Longitudinal Study Using Markerless Motion Tracking.Sensors (Basel, Switzerland) · 2026Article
- Evaluation Protocols and Validation for Cameras in Indoor Healthcare Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Infants' spontaneous movements explore arm dynamics.Communications biology · 2026Article
- A preregistered, open pipeline for early cerebral palsy risk assessment from infant videos.GigaScience · 2026Article
- A machine learning study highlighting the challenges of fidgety movement recognition using vision and inertial sensors.Scientific reports · 2026Article
- An Automated Framework for Pose-Sequence Analysis of Spontaneous Movements in Preterm Infants.IEEE open journal of engineering in medicine and biology · 2026Article
- Automatic infant 2D pose estimation from videos: Comparing seven deep neural network methods.Behavior research methods · 2025Article
- Evaluating Non-Invasive Computer Vision-Based Quantification of Neonatal Movement as a Marker of Development in Preterm Infants: A Pilot Study.Healthcare (Basel, Switzerland) · 2025Article
- Detection of epileptic spasms using foundational AI and smartphone videos.NPJ digital medicine · 2025Article
- Evaluating the impact of metabolic indicators and scores on cardiovascular events using machine learning.Diabetology & metabolic syndrome · 2025Article
- Comparison of marker-less 2D image-based methods for infant pose estimation.Scientific reports · 2025Article
- Can AI-Based Video Analysis Help Evaluate the Performance of the Items in the Bayley Scales of Infant Development?Children (Basel, Switzerland) · 2025Article
- Data-Driven Early Prediction of Cerebral Palsy Using AutoML and interpretable kinematic features.medRxiv : the preprint server for health sciences · 2025Article
- A systematic review of portable technologies for the early assessment of motor development in infants.NPJ digital medicine · 2025Article
- The importance of trunk motion in wearable based infant spontaneous movement analysis.Scientific reports · 2025Article
- Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study.EClinicalMedicine · 2024Article
- Quantifying Arm and Leg Movements in 3-Month-Old Infants Using Pose Estimation: Proof of Concept.Sensors (Basel, Switzerland) · 2024Article
- Quantifying spontaneous infant movements using state-space models.Scientific reports · 2024Article
- Design of a Robotic Infant Simulator to Understand the Role of the Trunk in Infant Postural Stability and Center of Pressure.RO-MAN ... : the ... IEEE International Symposium on Robot and Human Interactive Communication : proceedings. IEEE International Symposium on Robot and Human Interactive Communication · 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
9 authors.
Funding
Abstract
An infant's risk of developing neuromotor impairment is primarily assessed through visual examination by specialized clinicians. Therefore, many infants at risk for impairment go undetected, particularly in under-resourced environments. There is thus a need to develop automated, clinical assessments based on quantitative measures from widely-available sources, such as videos recorded on a mobile device. Here, we automatically extract body poses and movement kinematics from the videos of at-risk infants (N = 19). For each infant, we calculate how much they deviate from a group of healthy infants (N = 85 online videos) using a Naïve Gaussian Bayesian Surprise metric. After pre-registering our Bayesian Surprise calculations, we find that infants who are at high risk for impairments deviate considerably from the healthy group. Our simple method, provided as an open-source toolkit, thus shows promise as the basis for an automated and low-cost assessment of risk based on video recordings.
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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.