Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors
This work addresses the need for reliable and scalable automated analysis of infant motor performance to support developmental research and clinical decision-making, representing an incremental improvement.
The study tackled the problem of improving the accuracy of infant posture and movement classification using wearable sensors by investigating self-supervised pre-training and context-selective data screening, resulting in a robust accuracy increase and substantial further improvements in classifier performance.
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.