Deep learning empowered sensor fusion boosts infant movement classification
arxiv(2024)
摘要
There is a recent boom in the development of AI solutions to facilitate and
enhance diagnostic procedures for established clinical tools. 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) comparing 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. The performance of the three-sensor
fusion (classification accuracy of 94.5%) was significantly higher than that
of any single modality evaluated, suggesting the sensor fusion approach is a
promising avenue for automated classification of infant motor patterns. The
development of a robust sensor fusion system may significantly enhance AI-based
early recognition of neurofunctions, ultimately facilitating automated early
detection of neurodevelopmental conditions.
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