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.

该研究探讨了如何使用自监督的预训练方法来提高婴儿可穿戴设备的自动化分析的分类准确性，研究表明使用上下文相关的预训练数据可以大大提高分类器性能。