Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors
Manu Airaksinen, Okko R\"as\"anen, Elina Il\'en, Taru H\"ayrinen, Anna, Kivi, Viviana Marchi, Anastasia Gallen, Sonja Blom, Anni Varhe, Nico, Kaartinen, Leena Haataja, Sampsa Vanhatalo

TL;DR
This study introduces a wearable multi-sensor jumpsuit for infants that enables automated, accurate tracking of infant posture and movement, facilitating early detection of neurodevelopmental disorders.
Contribution
It develops a novel infant wearable device and a deep learning-based method for automatic movement classification, achieving human-level accuracy.
Findings
Achieved human-equivalent accuracy in movement classification.
Four-limb sensor configuration yields best performance.
Quantified human observer ambiguity to improve classifier training.
Abstract
Infants' spontaneous and voluntary movements mirror developmental integrity of brain networks since they require coordinated activation of multiple sites in the central nervous system. Accordingly, early detection of infants with atypical motor development holds promise for recognizing those infants who are at risk for a wide range of neurodevelopmental disorders (e.g., cerebral palsy, autism spectrum disorders). Previously, novel wearable technology has shown promise for offering efficient, scalable and automated methods for movement assessment in adults. Here, we describe the development of an infant wearable, a multi-sensor smart jumpsuit that allows mobile accelerometer and gyroscope data collection during movements. Using this suit, we first recorded play sessions of 22 typically developing infants of approximately 7 months of age. These data were manually annotated for infant…
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