AI Watches Baby Movements to Spot Cerebral Palsy Risk Earlier

Every year, millions of babies are born preterm, and they carry a substantially higher risk of developing cerebral palsy than infants born at term. The earlier clinicians can identify which infants are truly at risk, the earlier they can intervene with therapies that reshape developing neural pathways. For decades, the gold standard for early screening has been the Prechtl assessment of general movements, in which specially trained clinicians watch videos of a baby’s spontaneous wriggling and judge whether the movements show the fluid, variable quality known as fidgety movements. That judgment is powerful but scarce: it requires expert training, and it is subjective enough that inter-observer agreement can falter. A new pilot study from researchers in South Korea now reports a machine-learning framework that pairs camera-derived visual features with wearable motion sensors to classify fidgety versus non-fidgety movements automatically, and the results hint that a carefully engineered loss function may be the key to making such systems reliable.

The study, published in BioMedical Engineering OnLine, was conducted by a team led by Yeongjun Cho of the Biomedical Engineering Research Center at the Asan Institute for Life Sciences, together with colleagues from Asan Medical Center and the University of Ulsan College of Medicine in Seoul. Rather than attempting a full clinical trial, the authors framed their work explicitly as an internal methodological evaluation: a controlled, sample-level test of whether a particular training strategy could improve the way a neural network represents neonatal movement data. That framing matters, because it sets honest boundaries around what the results do and do not demonstrate, a distinction that is often lost in the breathless coverage of medical artificial intelligence.

The data came from eleven preterm neonates assessed at three to five months of corrected age, the age the babies would have reached had they been born at term. From these recordings, the researchers constructed 1,957 multimodal samples. Each sample combined two complementary views of the same movement episode. The first was a motion history image, or MHI, a computer-vision representation that collapses a short video sequence into a single image in which recent motion appears brighter and older motion fades, effectively freezing the spatial pattern of a baby’s movement into a form a vision model can digest. The second was a stream of inertial measurement unit, or IMU, signals from a wearable sensor, capturing the raw accelerations and rotations of the baby’s limbs that a camera might miss, especially when the infant is partially occluded or lying in an awkward position.

Every segment was labeled as fidgety movement or non-fidgety movement, and the labels, together with metadata about each segment, were organized in a unified manifest so that training, validation, and testing could be conducted in a controlled and reproducible way. The architecture at the heart of the framework was built on ImageBind, a multimodal embedding model designed to pull different sensory modalities into a shared representational space. In this shared space, a video-derived MHI feature and an IMU signal from the same movement episode should land close together, while samples from different movement classes should drift apart. Crucially, both training and final test-stage inference used only the MHI and IMU inputs, with the FM and nFM labels reserved for supervised loss optimization rather than being fed to the model as features.

The genuinely novel ingredient was the loss function, the mathematical objective that tells the network what it means to learn well. The team compared two established metric-learning losses and found each had complementary weaknesses. Triplet loss, a workhorse of metric learning, enforces local distance constraints: it pulls an anchor sample closer to samples of the same class than to samples of a different class by a defined margin. That local discipline is effective but myopic; it says nothing about the global geometry of the entire embedding space, and networks trained with it can develop distorted structures in which classes are separated in some regions and entangled in others. Tuned constellation loss, by contrast, shapes the global arrangement of embeddings but can leave local neighborhoods poorly constrained, allowing confusingly similar samples to sit near one another.

To resolve this tension, the researchers designed a combined loss that jointly enforces local distance constraints and global embedding structure. The intuition is architectural in the broadest sense: rather than choosing between a microscope and a telescope, the combined objective applies both simultaneously, demanding that individual samples obey local margins while the overall constellation of classes maintains a well-separated global layout. When the combined-loss model was evaluated on the predefined internal test partition, it outperformed both single-loss baselines, achieving an accuracy of 0.838, an F1-score of 0.828, an area under the receiver operating characteristic curve of 0.909, and an area under the precision-recall curve of 0.894. In a screening context, where the balance between catching true cases and avoiding false alarms is everything, the AUROC and AUPRC figures are particularly meaningful, since they summarize performance across all possible decision thresholds rather than at a single arbitrary cutoff.

Perhaps the most clinically relevant finding was the effect on class-wise balance. Screening tools fail in two distinct ways: they miss affected infants, or they flood clinics with false positives that erode trust and waste resources. Single-loss training in this dataset tended to sacrifice one class for the other, a failure mode that the combined loss appeared to mitigate. By shaping both the local and global geometry of the embedding space, the combined objective produced representations in which fidgety and non-fidgety samples were more evenly and reliably separated, suggesting that loss-function design, often treated as an engineering afterthought, can be a first-order determinant of clinical model behavior.

The authors are careful, and rightly so, about the limits of what this pilot demonstrates. The evaluation was sample-level and internal: the 1,957 test samples were drawn from the same eleven infants whose data trained the model, even if individual samples were held out. That means the reported metrics measure how well the framework learns to represent movement patterns it has, in some sense, already seen variants of, not how well it generalizes to a completely new baby. Neonatal movement data is notoriously subject-dependent; each infant has a unique movement signature shaped by gestational age, postnatal course, and temperament, and models can exploit these idiosyncrasies as shortcuts. The team explicitly states that their findings motivate further validation in larger subject-held-out cohorts, where the test infant is entirely unseen during training. Until such studies are done, the 0.909 AUROC should be read as a proof of concept rather than a clinical performance figure.

Even with those caveats, the study’s design choices point toward a practical future for the technology. The reliance on ordinary video plus a lightweight wearable sensor means the data acquisition could, in principle, happen in a clinic or even at home, without the expensive and stressful infrastructure of a dedicated motion-capture laboratory. The unified manifest approach, in which labels and segment metadata are organized alongside the raw modalities, reflects a growing recognition in biomedical machine learning that rigorous data governance is as important as model architecture. And the finding that combining losses improves robustness is a transferable lesson for the wider field of multimodal health AI, where researchers frequently must fuse signals as different as pixels and accelerations.

The work, approved by the Institutional Review Board of Asan Medical Center and registered with the Clinical Research Information Service, was funded by Korea’s Ministry of Trade, Industry and Energy, the National Research Foundation of Korea, Asan Medical Center, and the Korean Academy of Rehabilitation Medicine. Its significance lies less in any single number than in the demonstration that fidgety movement, a subtle qualitative phenomenon long accessible only to trained human eyes, can be encoded in a shared multimodal representation learned from cameras and inertial sensors. If subsequent subject-held-out studies replicate and extend these results, the quiet wriggling of a preterm infant, recorded for a few minutes on an ordinary camera and a small wearable, could become one of the earliest and most accessible windows into neurological risk, giving clinicians and families time, the resource that matters most in early intervention.

Subject of Research: Multimodal machine learning for automated classification of fidgety movements in preterm neonates as an early screen for cerebral palsy risk

Article Title: An internal methodological evaluation of a multimodal VIDEO-IMU metric-learning framework for fidgety movement classification in preterm neonates: a controlled sample-level study

Article References: An internal methodological evaluation of a multimodal VIDEO-IMU metric-learning framework for fidgety movement classification in preterm neonates: a controlled sample-level study. (n.d.). https://doi.org/10.1186/s12938-026-01638-y

Image Credits: AI Generated

DOI: 10.1186/s12938-026-01638-y

Keywords: fidgety movements, preterm neonates, cerebral palsy, machine learning, metric learning, multimodal learning, inertial measurement unit, motion history image, loss function, neonatology, biomedical engineering, early screening