Approaches, Applications, and Challenges in Physiological Emotion Recognition-A Tutorial Overview

An automatic emotion recognition system can serve as a fundamental framework for various applications in daily life from monitoring emotional well-being to improving the quality of life through better emotion regulation. Understanding the process of emotion manifestation becomes crucial for building emotion recognition systems. An emotional experience results in changes not only in interpersonal behavior but also in physiological responses. Physiological signals are one of the most reliable means for recognizing emotions since individuals cannot consciously manipulate them for a long duration. These signals can be captured by medical-grade wearable devices, as well as commercial smart watches and smart bands. With the shift in research direction from laboratory to unrestricted daily life, commercial devices have been employed ubiquitously. However, this shift has introduced several challenges, such as low data quality, dependency on subjective self-reports, unlimited movement-related changes, and artifacts in physiological signals. This tutorial provides an overview of practical aspects of emotion recognition, such as experiment design, properties of different physiological modalities, existing datasets, suitable machine learning algorithms for physiological data, and several applications. It aims to provide the necessary psychological and physiological backgrounds through various emotion theories and the physiological manifestation of emotions, thereby laying a foundation for emotion recognition. Finally, the tutorial discusses open research directions and possible solutions.

Automated summary

An automatic emotion recognition system is essential for various daily life applications, from monitoring emotional well-being to improving quality of life through better emotion regulation. Recognizing emotions based on physiological signals provides a reliable means, which can be captured by wearable devices like smart watches. However, the shift from laboratory to daily life research presents challenges such as low data quality, subjective self-reports, movement-related changes, and artifacts in physiological signals.