The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings. This information could be used for developmental research and to support clinical decision-making, such as detection of developmental problems and guiding of their therapeutic interventions. MAIJU-based analyses rely fully on the classification of infant's posture and movement; it is hence essential to study ways to increase the accuracy of such classifications, aiming to increase the reliability and robustness of the automated analysis. Here, we investigated how self-supervised pre-training improves performance of the classifiers used for analyzing MAIJU recordings, and we studied whether performance of the classifier models is affected by context-selective quality-screening of pre-training data to exclude periods of little infant movement or with missing sensors. Our experiments show that i) pre-training the classifier with unlabeled data leads to a robust accuracy increase of subsequent classification models, and ii) selecting context-relevant pre-training data leads to substantial further improvements in the classifier performance.

翻译：最新研发的婴儿可穿戴设备MAIJU能够在院外环境中以客观且可扩展的方式自动评估婴儿运动表现。这些信息可用于发育研究及支持临床决策，例如发育问题的检测及其治疗干预的指导。基于MAIJU的分析完全依赖于婴儿姿势与运动的分类，因此研究提升此类分类准确性的方法至关重要，旨在提高自动分析的可靠性和鲁棒性。本研究探讨了自监督预训练如何提升用于分析MAIJU记录的分类器性能，并研究了分类器模型性能是否受预训练数据的情景选择性质量筛选（排除婴儿运动较少或传感器缺失时段）的影响。实验表明：i) 使用未标注数据对分类器进行预训练可显著提升后续分类模型的准确性；ii) 选择情景相关预训练数据能进一步大幅改善分类器性能。