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 the diagnosis of
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. 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 early implementation of automated
detection of neurodevelopmental conditions.

通过传感器融合的方法，对婴儿的运动模式进行自动分类可能显著提高基于人工智能的神经功能的早期识别，最终促进神经发育状况的自动化检测。