Towards Automated Emotion Classification of Atypically and Typically Developing Infants.

TitleTowards Automated Emotion Classification of Atypically and Typically Developing Infants.
Publication TypeJournal Article
Year of Publication2020
AuthorsLysenko S, Seethapathi N, Prosser L, Kording K, Johnson MJ
JournalProc IEEE RAS EMBS Int Conf Biomed Robot Biomechatron
Volume2020
Pagination503-508
Date Published2020 Nov-Dec
ISSN2155-1774
Abstract

The World Health Organization estimates that 15 million infants are born preterm every year [1]. This is of concern because these infants have a significant chance of having neuromotor or cognitive developmental delays due to cerebral palsy or other developmental issues [2]. Our long-term goal is to determine the roles emotion and movement play in the diagnosis of atypical infants. In this paper, we examine how automated emotion assessment may have potential to classify typically and atypically developing infants. We compare a custom supervised machine learning algorithm that utilizes individual and grouped facial features for infant emotion classification with a state-of-the-art neural network. Our results show that only three concavity features are needed for the concavity algorithm, and the custom algorithm performed with relatively similar performance to the neural network. Automatic sentiment labels used in tandem with infant movement kinematics would be further investigated to determine if emotion and movement are interdependent and predictive of an infant's neurodevelopmental delay in disorders such as cerebral palsy.

DOI10.1109/BioRob49111.2020.9224271
Alternate JournalProc IEEE RAS EMBS Int Conf Biomed Robot Biomechatron
PubMed ID33959406
PubMed Central IDPMC8099034
Grant ListR01 HD097686 / HD / NICHD NIH HHS / United States
R21 HD084327 / HD / NICHD NIH HHS / United States