Automated Affect and Emotion Recognition from Cardiovascular Signals - A Systematic Overview Of The Field

Currently, artificial intelligence is increasingly used to recognize and differentiate emotions. Through the action of the nervous system, the heart and vascular system can respond differently depending on the type of arousal. With the growing popularity of wearable devices able to measure such signals, people may monitor their states and manage their wellness. Our goal was to explore and summarize the field of automated emotion and affect recognition from cardiovascular signals. According to our protocol, we searched electronic sources (MEDLINE, EMBASE, Web of Science, Scopus, dblp, Cochrane Library, IEEE Explore, arXiv and medRxiv) up to 31 August 2020. In the case of all identified studies, two independent reviewers were involved at each stage: screening, full-text assessment, data extraction, and quality evaluation. All conflicts were resolved during the discussion. The credibility of included studies was evaluated using a proprietary tool based on QUADAS, PROBAST. After screening 4649 references, we identified 195 eligible studies. From artificial intelligence most used methods in emotion or affect recognition were Support Vector Machines (42.86%), Neural Network (21.43%), and k-Nearest Neighbors (11.67%). Among the most explored datasets were DEAP (10.26%), MAHNOB-HCI (10.26%), AMIGOS (6.67%) and DREAMER (2.56%). The most frequent cardiovascular signals involved electrocardiogram (63.16%), photoplethysmogram (15.79%), blood volume pressure (13.16%) and heart rate (6.58%). Sadness, fear, and anger were the most examined emotions. However, there is no standard set of investigated internal feelings. On average, authors explore 4.50 states (range from 4 to 24 feelings). Research using artificial intelligence in recognizing emotions or affect using cardiovascular signals shows an upward trend. There are significant variations in the quality of the datasets, the choice of states to detect, and the classifiers used for analysis. Research project supported by program Excellence initiative - research university for the University of Science and Technology. The authors declare that they have no conflict of interest

Datasets for Automated Affect and Emotion Recognition from Cardiovascular Signals Using Artificial Intelligence— A Systematic Review

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

Versatility of Saccharomyces cerevisiae 41CM in the Brewery Sector: Use as a Starter for “Ale” and “Lager” Craft Beer Production

Craft breweries tend to use special raw materials and also special ingredients (spices, herbs, fruits) to typify beers, but the metabolic activities of yeasts play a primary role in defining the sensory characteristics of this beverage. Saccharomyces cerevisiae and Saccharomyces pastorianus are yeast species usually used for ale and lager beer production. The selection and use of new yeast starters with peculiar technological and enzymatic characteristics could represent the key point for the production of beers with good and distinctive organoleptic properties. In this study, the fermentative performance of S. cerevisiae 41CM yeast isolated from the vineyard environment for ale and lager craft beer production on a laboratory scale was evaluated. The commercial yeast S. cerevisiae Fermentis S-04 and S. pastorianus Weihenstephan 34/70 were used as reference strains. S. cerevisiae 41CM showed fermentative kinetics similar to commercial starters, both in lager (12 °C) and ale (20 °C) brewing. In all beers brewed, the largest percentage of volatile compounds synthesized during the fermentation were alcohols, followed by esters, terpenes, and aldehydes. In particular, S. cerevisiae 41CM starter contributed a higher relative percentage of esters in the ale beer than that detected in the lager beer, without ever synthesizing unwanted volatile compounds