An automatic sustained attention prediction (ASAP) method for infants and toddlers using wearable device signals

Sustained attention (SA) is a critical cognitive ability that emerges in infancy and affects various aspects of development. Research on SA typically occurs in lab settings, which may not reflect infants' real-world experiences. Infant wearable technology can collect multimodal data in natural environments, including physiological signals for measuring SA. Here we introduce an automatic sustained attention prediction (ASAP) method that harnesses electrocardiogram (ECG) and accelerometer (Acc) signals. Data from 75 infants (6- to 36-months) were recorded during different activities, with some activities emulating those occurring in the natural environment (i.e., free play). Human coders annotated the ECG data for SA periods validated by fixation data. ASAP was trained on temporal and spectral features from the ECG and Acc signals to detect SA, performing consistently across age groups. To demonstrate ASAP's applicability, we investigated the relationship between SA and perceptual features-saliency and clutter-measured from egocentric free-play videos. Results showed that saliency in infants' and toddlers' views increased during attention periods and decreased with age for attention but not inattention. We observed no differences between ASAP attention detection and human-coded SA periods, demonstrating that ASAP effectively detects SA in infants during free play. Coupled with wearable sensors, ASAP provides unprecedented opportunities for studying infant development in real-world settings.