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%) was significantly higher than that of any single modality evaluated. We show that 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.

翻译：为评估发育中神经系统的完整性，普雷希特全身运动评估（GMA）因其在早期婴儿神经损伤诊断中的临床价值而获得公认。通过机器学习方法增强GMA的应用日益增多，旨在扩大其应用范围、规避培训人类评估者的成本，并进一步标准化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，其性能仍远逊于训练有素的人类评估者。由于所有模型均在专有/孤立数据集上设计、训练和评估，这些方法之间难以进行有效比较。本研究提出了一种用于评估不安运动（FMs）的传感器融合方法。数据采集自51名典型发育的参与者，我们比较了三种不同的传感器模态（压力传感器、惯性传感器和视觉传感器）。通过测试多种传感器组合及两种融合策略（后期融合与早期融合），评估多传感器系统是否优于单模态评估。研究采用卷积神经网络（CNN）架构对运动模式进行分类。三传感器融合的性能（分类准确率达94.5%）显著优于任何单一模态的评估结果。研究表明，传感器融合方法是实现婴儿运动模式自动化分类的有效途径。开发稳健的传感器融合系统可显著提升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的自动化早期检测。