AIARS: Towards Automated Infant Activity Recognition System using Machine Learning and Multi-sensor Fusion Data

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dc.contributor.authorThelagathoti, Rama Krishna
dc.contributor.authorChaudhary, Priyanka
dc.contributor.authorKnarr, Brian
dc.contributor.authorSchenkelberg, Michaela
dc.contributor.authorYoun, Jong-Hoon
dc.contributor.authorAli, Hesham
dc.contributor.authorDinkel, Danae
dc.date.accessioned2024-12-26T21:07:04Z
dc.date.available2024-12-26T21:07:04Z
dc.date.issued2025-01-07
dc.description.abstractInfant Activity Recognition (IAR) plays a pivotal role in modern healthcare and developmental studies,offering crucial insights into early childhood behavior and motor development. Nevertheless, identifying various categories of infant activities such as playing,crawling, and feeding, poses challenges due to the dynamic nature of a child’s age and the continuous involvement of parents or caregivers. Recognizing infant activities aids in the timely detection of motor disorders and promotes healthy movement behavior. In this study,we propose an automated Infant Activity Recognition System (AIARS) utilizing a Machine Learning (ML)approach that leverages data from wearable sensors.Initially, we compiled an Infant Activity Database(IAD) by collecting data from infants across 16 distinct activities in controlled laboratory environments. Due to the limited sample size for each activity, we aggregated these 16 activities into 3 and then 2 broader categories.Employing ML techniques, including Random Forest(RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB), we developed an AIARS to classify various infant activities. Our study achieved impressive results, with an accuracy of 93.85% , an Area Under the Curve (AUC) of 99%, and an F1 score of 95.2%. We also addressed the challenges faced while applying ML methods in developing AIARS and provided recommendations to mitigate these challenges.These findings underscore the efficacy of our approach and represent a significant milestone in the domain of IAR.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.394
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.otherd7857f72-a478-4f19-951d-b8d728ca788e
dc.identifier.urihttps://hdl.handle.net/10125/109236
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBody Sensor Networks for Personalized Medicine
dc.subjectaccelerometer, infant activity classification, infant activity recognition, machine learning, physical activity
dc.titleAIARS: Towards Automated Infant Activity Recognition System using Machine Learning and Multi-sensor Fusion Data
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage3254

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