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

TL;DR

This survey reviews recent neural network-based methods for emotion recognition in healthcare, focusing on speech, facial expressions, and audio-visual data to enable patient monitoring and early intervention.

Contribution

It systematically analyzes recent research on neural networks for emotion recognition in healthcare, highlighting deployment techniques and future challenges.

Findings

Neural networks effectively recognize emotions from speech, facial expressions, and audio-visual data.

Emotion recognition can assist in early diagnosis of depression and stress.

Various deployment methods for real-world healthcare surveillance are discussed.

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.