Emotion Recognition from Peripheral Physiological Signals: A Systematic Review of Trends, Challenges and Opportunities

The adaptability and intuitiveness of Human-Computer-Interaction systems are enhanced by emotion recognition capabilities, whose rapid advancement asks for updated and more complete surveys. In this comprehensive work, papers using at least one of three peripheral physiological signals (galvanic skin response, heart rate and respiration signals) were identified, resulting in 386 papers and 448 studies that were reviewed according to the entire emotion recognition pipeline, and not just based on types of signals and recognition methods as done in related work. Accordingly, this review identifies trends, challenges and opportunities across different aspects of the emotion recognition literature. Our investigation showed that multimodal approaches, benefitting from complementary physiological information, dominate the literature. Emotion-inducing methods tend to be dynamic and to progress towards real-life applications. To facilitate such applications, building novel datasets should be considered. For instance, there is room for novel continuously annotated datasets to facilitate the development of dynamic emotion models—which is also crucial for reliable real-life applications. At the same time, to guarantee a reliable continuous annotation, the combination of stimuli and assessment/report method should not be too overwhelming for the studies’ participants. Our results showed that support vector machines remain prevalent among traditional machine learning methods, but the growth of deep learning methods used either for feature extraction or end-to-end recognition is evident—both in number of studies and advanced developed techniques. Although a balance between algorithms’ performance and interpretability is essential in emotion recognition, there is a noticeable gap in integrating emotion theory into algorithms, which would improve such balance. Besides bringing to light a broad panorama of the literature, this work offers a digital table with the analysis of all studies and a filter possibility, allowing researchers to take advantage of it to accelerate and/or get inspiration for their own work.