Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review

• URL: http://arxiv.org/abs/2407.04712v1
• Date: Wed, 15 May 2024 19:48:04 GMT
• Title: Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review
• Authors: Oresti Banos, Zhoe Comas-González, Javier Medina, Aurora Polo-Rodríguez, David Gil, Jesús Peral, Sandra Amador, Claudia Villalonga,
• Abstract summary: This study reviews the literature on the use of Human Emotion Recognition (HER) systems in autism. We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines.
• Score: 0.624304828756181
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people. Objective: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. Methods: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults. Results: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review. Conclusions: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models.

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