TY  - JOUR
AU  - Kulvicius, Tomas
AU  - Zhang, Dajie
AU  - Poustka, Luise
AU  - Bölte, Sven
AU  - Jahn, Lennart
AU  - Flügge, Sarah
AU  - Kraft, Marc
AU  - Zweckstetter, Markus
AU  - Nielsen-Saines, Karin
AU  - Wörgötter, Florentin
AU  - Marschik, Peter B
TI  - Deep learning empowered sensor fusion boosts infant movement classification.
JO  - Communications medicine
VL  - 5
IS  - 1
SN  - 2730-664X
CY  - [London]
PB  - Springer Nature
M1  - DZNE-2025-00159
SP  - 16
PY  - 2025
AB  - To assess the integrity of the developing nervous system, the Prechtl general movement assessment (GMA) is recognized for its clinical value in diagnosing neurological impairments in early infancy. GMA has been increasingly augmented through machine learning approaches intending to scale-up its application, circumvent costs in the training of human assessors and further standardize classification of spontaneous motor patterns. Available deep learning tools, all of which are based on single sensor modalities, are however still considerably inferior to that of well-trained human assessors. These approaches are hardly comparable as all models are designed, trained and evaluated on proprietary/silo-data sets.With this study we propose a sensor fusion approach for assessing fidgety movements (FMs). FMs were recorded from 51 typically developing participants. We compared three different sensor modalities (pressure, inertial, and visual sensors). Various combinations and two sensor fusion approaches (late and early fusion) for infant movement classification were tested to evaluate whether a multi-sensor system outperforms single modality assessments. Convolutional neural network (CNN) architectures were used to classify movement patterns.The performance of the three-sensor fusion (classification accuracy of 94.5
LB  - PUB:(DE-HGF)16
C6  - pmid:39809877
C2  - pmc:PMC11733215
DO  - DOI:10.1038/s43856-024-00701-w
UR  - https://pub.dzne.de/record/275937
ER  -