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arXiv · 2107.05989

Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey

Marwan Dhuheir·Abdullatif Albaseer·Emna Baccour·Aiman Erbad·Mohamed Abdallah·Mounir Hamdi
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Abstract

Recognizing the patient's emotions using deep learning techniques has attracted significant attention recently due to technological advancements. Automatically identifying the emotions can help build smart healthcare centers that can detect depression and stress among the patients in order to start the medication early. Using advanced technology to identify emotions is one of the most exciting topics as it defines the relationships between humans and machines. Machines learned how to predict emotions by adopting various methods. In this survey, we present recent research in the field of using neural networks to recognize emotions. We focus on studying emotions' recognition from speech, facial expressions, and audio-visual input and show the different techniques of deploying these algorithms in the real world. These three emotion recognition techniques can be used as a surveillance system in healthcare centers to monitor patients. We conclude the survey with a presentation of the challenges and the related future work to provide an insight into the applications of using emotion recognition.

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Marwan Dhuheir, Abdullatif Albaseer, Emna Baccour, Aiman Erbad, Mohamed Abdallah, Mounir Hamdi. 2021-07-13. Emotion Recognition for Healthcare Surveillance Systems Using Neural Networks: A Survey. https://arxiv.org/abs/2107.05989

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