71 citations · 121 across the 7 of their papers we have counts for
7 papers · 1 filter
Toward Reliable Infant Pose Estimation: A Training-Dynamics Approach to Noisy Annotation Detection
Emanuele Cardinale, Sara Moccia, Alessandro Cacciatore +1
Spontaneous movement analysis in preterm infants relies increasingly on markerless pose estimation (PE) to derive clinically relevant motion biomarkers directly from video recordin…
The Blind Spot in 2D Infants' Pose Estimation:Robust Learning from Noisy Annotations
Emanuele Cardinale, Marco Proietti, Alessandro Cacciatore +3
Noisy annotations pose a significant challenge for supervised deep learning, as neural networks rely on large-scale, high-quality labeled data whose corruption can severely impair…
A preliminary study on continual learning in computer vision using Kolmogorov-Arnold Networks
Alessandro Cacciatore, Valerio Morelli, Federica Paganica +3
Deep learning has long been dominated by multi-layer perceptrons (MLPs), which have demonstrated superiority over other optimizable models in various domains. Recently, a new alter…
Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation
Carolina Gonçalves, João M. Lopes, Sara Moccia +3
Gait disabilities are among the most frequent worldwide. Their treatment relies on rehabilitation therapies, in which smart walkers are being introduced to empower the user's recov…
Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks
Manuel Palermo, Sara Moccia, Lucia Migliorelli +2
Rehabilitation is important to improve quality of life for mobility-impaired patients. Smart walkers are a commonly used solution that should embed automatic and objective tools fo…
Preterm infants' pose estimation with spatio-temporal features
Sara Moccia, Lucia Migliorelli, Virgilio Carnielli +1
Objective: Preterm infants' limb monitoring in neonatal intensive care units (NICUs) is of primary importance for assessing infants' health status and motor/cognitive development.…
Preterm infants' limb-pose estimation from depth images using convolutional neural networks
Sara Moccia, Lucia Migliorelli, Rocco Pietrini +1
Preterm infants' limb-pose estimation is a crucial but challenging task, which may improve patients' care and facilitate clinicians in infant's movements monitoring. Work in the li…