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.

翻译：近年来，人工智能解决方案的开发蓬勃发展，旨在促进和增强现有临床工具的诊断流程。为评估发育中神经系统的完整性，Prechtl全身运动评估（GMA）因其在婴儿早期神经损伤诊断中的临床价值而获得认可。通过机器学习方法的不断融合，GMA的应用范围得以扩大，旨在降低培训人类评估员的成本，并进一步规范自发运动模式的分类。然而，现有的深度学习工具（均基于单一传感器模态）仍远逊于训练有素的人类评估员。由于所有模型均在专有/孤立数据集上设计、训练和评估，这些方法之间难以直接比较。本文提出一种用于评估烦躁运动（FMs）的传感器融合方法，比较了三种不同的传感器模态（压力、惯性和视觉传感器）。我们测试了多种组合及两种传感器融合方法（晚期融合与早期融合）在婴儿运动分类中的表现，以评估多传感器系统是否优于单模态评估。三传感器融合的性能（分类准确率达94.5%）显著高于任何单一模态的评估结果，表明传感器融合方法是实现婴儿运动模式自动分类的一条可行途径。开发鲁棒的传感器融合系统有望显著增强基于人工智能的神经功能早期识别能力，最终推动神经发育状况自动检测技术的早期应用。