Systematic Review of Affective Computing Techniques for Infant Robot Interaction

作     者：Filippini, Chiara Merla, Arcangelo 

作者机构：Univ G dAnnunzio Chieti Dept Neurosci Imaging & Clin Sci Pescara Italy Next2U srl Pescara Italy 

出 版 物：《INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS》 (社会机器人国际杂志)

年 卷 期：2023年第15卷第3期

页      面：393-409页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0811[工学-控制科学与工程] 

基　　金：PON MIUR SI-ROBOTICS [ARS01_01120] Universita degli Studi G. D'Annunzio Chieti Pescara within the CRUI-CARE Agreement 

主　　题：Infant-robot interaction Affective computing Developmental robotics Affective states Psychophysiology 

摘      要：Research studies on social robotics and human-robot interaction have gained insights into factors that influence people s perceptions and behaviors towards robots. However, adults perceptions of robots may differ significantly from those of infants. Consequently, extending this knowledge also to infants attitudes toward robots is a growing field of research. Indeed, infant-robot interaction (IRI) is emerging as a critical and necessary area of research as robots are increasingly used in social environments, such as caring for infants with all types of disabilities, companionship, and education. Although studies have been conducted on the ability of robots to positively engage infants, little is known about the infants affective state when interacting with a robot. In this systematic review, technologies for infant affective state recognition relevant to IRI applications are presented and surveyed. Indeed, adapting techniques currently employed for infant s emotion recognition to the field of IRI results to be a complex task, since it requires timely response while not interfering with the infant s behavior. Those aspects have a crucial impact on the selection of the emotion recognition techniques and the related metrics to be used for this purpose. Therefore, this review is intended to shed light on the advantages and the current research challenges of the infants affective state recognition approaches in the IRI field, elucidates a roadmap for their use in forthcoming studies as well as potentially provide support to future developments of emotion-aware robots.