Emotional recognition technologies applied to health: review and challenges.

Abstract

Objective: Emotions affect health and health affects emotions. When properly recognized and interpreted, emotions can aid in the prevention, diagnosis, and treatment of many diseases. Affective computing, the use of computer technology to detect one or more signals associated with human emotions, is a promising field. However, research into the use of emotion recognition in healthcare remains scarce. It is crucial to explore the reasons behind this phenomenon and identify neglected methods with potential for human health. We review methods and technologies used in emotion recognition and their applications in healthcare, highlighting methods not discussed in previous reviews, including electroencephalography and electrocardiography, thermal imaging, bracelets, skin conductance, and audio.

Methods: A metric based on reproducibility and population was established to assess the quality of included articles. Based on the metrics established, we surveyed and analyzed studies in which affective computing tools were applied to health, to qualify and identify the challenges of the area.

Results: We found many challenges to be overcome in detecting and recognizing human emotions, related to sample size, low study quality, and reproducibility issues. We list and discuss the main current challenges, ways to overcome them, and perspectives for the future, focusing on the application of affective technologies in healthcare and the establishment of a gold standard.

Conclusion: Three suggestions are proposed: 1) to conduct studies focused on obtaining a gold standard; 2) to conduct studies with larger sample sizes, greater diversity, and in less controlled environments, using replicable methodologies and making data and methods available; and 3) to further explore the potential use of emotion detection in healthcare.