  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.
