A Comprehensive Study on Emotion Recognition in Healthcare by Applying Machine Learning and Deep Learning Techniques

The capacity of exactly infer human feelings from enlarger modalities and sources called emotion recognition with the help of physiological signals, physical signals and questionnaires. The diverse application fields such as healthcare, market research, human-robot interactions and affective computing to make huge attention in emotion recognition. Nowadays, human emotions are analyzed and recognized based on the growth of information technology and sensors machines. Different manifestations present in human emotions. The examination of physiological signals, behavior, speech and facial expressions realizes the emotion recognition. The growth of affective computing promoted with accurate recognition human emotions in which different sensors collect the human emotions. This work reviewed various kinds of techniques for emotion recognition in healthcare system (ERHS). More than 35 papers selected and reviewed literature research methodologies. According to various innovations, this survey is classified into machine and deep learning (ML and DL) models. For recognizing emotions, the dataset details and various methods with its merits and demerits are focused in this article. The existing literature with its potential challenges and also the future research direction is reviewed.