A systematic review of 18 datasets for automated affect and emotion recognition using cardiovascular signals found that dataset quality was mainly low, with ECG being the most explored signal (83.33%).
Systematic Review (n=812)
The systematic review highlights that while ECG is frequently used for AI-based emotion recognition, the quality of publicly available datasets remains low, necessitating better methodological reporting.
Our review aimed to assess the current state and quality of publicly available datasets used for automated affect and emotion recognition (AAER) with artificial intelligence (AI), and emphasising cardiovascular (CV) signals. The quality of such datasets is essential to create replicable systems for future work to grow. We investigated nine sources up to 31 August 2020, using a developed search strategy, including studies considering the use of AI in AAER based on CV signals. Two independent reviewers performed the screening of identified records, full-text assessment, data extraction, and credibility. All discrepancies were resolved by discussion. We descriptively synthesised the results and assessed their credibility. The protocol was registered on the Open Science Framework (OSF) platform. Eighteen records out of 195 were selected from 4649 records, focusing on datasets containing CV signals for AAER. Included papers analysed and shared data of 812 participants aged 17 to 47. Electrocardiography was the most explored signal (83.33% of datasets). Authors utilised video stimulation most frequently (52.38% of experiments). Despite these results, much information was not reported by researchers. The quality of the analysed papers was mainly low. Researchers in the field should concentrate more on methodology.
Jemioło et al. (2022) conducted a systematic review in Automated affect and emotion recognition (n=812). Automated affect and emotion recognition with artificial intelligence using cardiovascular signals was evaluated on Current state and quality of publicly available datasets. A systematic review of 18 datasets for automated affect and emotion recognition using cardiovascular signals found that dataset quality was mainly low, with ECG being the most explored signal (83.33%).