Automatic Emotion Recognition in Clinical Scenario: A Systematic Review of Methods

Abstract

Automatic emotion recognition has powerful opportunities in the clinical field, but several critical aspects are still open, such as heterogeneity of methodologies or technologies tested mainly on healthy people. This systematic review aims to survey automatic emotion recognition systems applied in real clinical contexts, to deeply analyse clinical and technical aspects, how they were addressed, and relationships among them. The literature review was conducted on: IEEEXplore, ScienceDirect, Scopus, PubMed, ACM. Inclusion criteria were the presence of an automatic emotion recognition algorithm and the enrollment of at least 2 patients in the experimental protocol. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Moreover, the works were analysed according to a reference model to deeply examine both clinical and technical topics. 52 scientific papers passed inclusion criteria. Most clinical scenarios involved neurodevelopmental, neurological and psychiatric disorders with the aims of diagnosing, monitoring, or treating emotional symptoms. The most adopted signals are video and audio, while supervised shallow learning is mostly used for emotion recognition. A poor study design, tiny samples, and the absence of a control group emerged as methodological weaknesses. Heterogeneity of performance metrics, datasets and algorithms challenges results comparability, robustness, reliability and reproducibility.