Towards Automated Emotion Classification of Atypically and Typically Developing Infants.
Towards Automated Emotion Classification of Atypically and Typically Developing Infants.
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DOI：
10.1109/biorob49111.2020.9224271
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发表时间：
2020-11
期刊：
影响因子：
--
通讯作者：
Johnson MJ
中科院分区：
文献类型：
--
作者：
Lysenko S;Seethapathi N;Prosser L;Kording K;Johnson MJ
The World Health Organization estimates that 15 million infants are born preterm every year. 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. 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.
