Emotion recognition involves accurately interpreting human emotions from various sources and modalities, including questionnaires, verbal, and physiological signals. With its broad applications in affective computing, computational creativity, human-robot interactions, and market research, the field has seen a surge in interest in recent years. This paper presents a systematic review of multimodal emotion recognition (mer) techniques developed from 2014 to 2024, encompassing verbal, physiological signals, facial, body gesture, and speech as well as emerging methods like sketches emotion recognition. The review explores various emotion models, distinguishing between emotions, feelings, sentiments, and moods, along with human emotional expression, categorized in both artistic and non-verbal ways. It also discusses the background of automated emotion recognition systems and introduces seven criteria for evaluating modalities alongside a current state analysis of mer, drawn from the human-centric perspective of this field. By selecting the PRISMA guidelines and carefully analyzing 45 selected articles, this review provides comprehensive perspectives into existing studies, datasets, technical approaches, identified gaps, and future directions in MER. It also highlights existing challenges and potential applications of the MER.

The demands and preferences of men are often prioritized in societal challenges, despite women comprising half of the world’s population. Gender-Responsive Research and Innovation (GRRI) offers an approach to address this bias, promoting comprehensive and equitable solutions to better-understood real-world problems. The successful implementation of GRRI depends on recognizing obstacles, staying informed about relevant initiatives, and using this knowledge to develop effective strategies, ultimately contributing to a fairer and more responsive society. This research, using a mixed-methods approach, identified 10 critical GRRI concerns, measures, and strategies to address them, as well as 18 noteworthy initiatives across research, industrial, and regulatory contextual fields. It also enhances the understanding of GRRI, laying the groundwork for an ontology of GRRI that can benefit policymakers, organizations, and researchers seeking more inclusive strategies and practices.

Biologicalisation defines the analysis of biological patterns as a source of inspiration to model intelligent manufacturing systems. Due to their inherent adaptability, these design representations are highly desirable considering the increasing complexity of modeling current manufacturing solutions. Contributing to the idea of self-organizing and autonomous shop floors, we present a framework that aims to support the collaborative healing of manufacturing resources. This has been inspired on the altruistic behavior of bats in which social care is demonstrated (e.g., in bat colonies) even at the cost of individuals’ fitness/health. These ideas are conceptually showcased in an emergent automation manufacturing application, i.e., peer-to-peer energy sharing in automated guided vehicles. Some conclusions, and potential future research are discussed at the end of the paper.

Creativity and emotion are fundamental aspects of human expression, interaction, and innovation, shaping various facets of modern society. This paper explores the intersection of technology, human creativity and emotional expression by proposing a novel approach to detecting creative states through the analysis of emotional cues derived from speech. To validate the findings, a linguistic prompt creativity test was conducted, incorporating both human evaluations and AI-driven assistance as the co-collaborative approach, using the Torrance Test of Creative Thinking (TTCT) as the evaluation criteria. The study analysed 50 creative speech samples from 25 participants, each exposed to happy and sad video stimuli to evoke distinct emotional responses. The results reveal a correlation between emotional states and creativity. In 80% of the participants, higher creativity levels were observed when they experienced positive emotions and happiness, highlighting the significant influence of emotional context on creative expression and creative states and also aligning with the psychological research on exploring the effects of emotions on creativity.