Showing papers by "Polytechnic University of Valencia published in 2020"

Programmable photonic circuits.

Abstract: The growing maturity of integrated photonic technology makes it possible to build increasingly large and complex photonic circuits on the surface of a chip. Today, most of these circuits are designed for a specific application, but the increase in complexity has introduced a generation of photonic circuits that can be programmed using software for a wide variety of functions through a mesh of on-chip waveguides, tunable beam couplers and optical phase shifters. Here we discuss the state of this emerging technology, including recent developments in photonic building blocks and circuit architectures, as well as electronic control and programming strategies. We cover possible applications in linear matrix operations, quantum information processing and microwave photonics, and examine how these generic chips can accelerate the development of future photonic circuits by providing a higher-level platform for prototyping novel optical functionalities without the need for custom chip fabrication. The current state of programmable photonic integrated circuits is discussed, including recent developments in their building blocks, circuit architectures, electronic control and programming strategies, as well as different application spaces.

Photocatalytic CO2 Reduction to C2+Products

Abstract: There is a considerable interest in the development of photocatalytic CO2 conversion by sunlight, since this process has similarities with natural photosynthesis on which life on Earth is based. At...

IJER editorial: The future of the internal combustion engine:

Abstract: Internal combustion (IC) engines operating on fossil fuel oil provide about 25% of the world’s power (about 3000 out of 13,000 million tons oil equivalent per year—see Figure 1), and in doing so, they produce about 10% of the world’s greenhouse gas (GHG) emissions (Figure 2). Reducing fuel consumption and emissions has been the goal of engine researchers and manufacturers for years, as can be seen in the two decades of ground-breaking peer-reviewed articles published in this International Journal of Engine Research (IJER). Indeed, major advances have been made, making today’s IC engine a technological marvel. However, recently, the reputation of IC engines has been dealt a severe blow by emission scandals that threaten the ability of this technology to make significant and further contributions to the reduction of transportation sector emissions. In response, there have been proposals to replace vehicle IC engines with electric-drives with the intended goals of further reducing fuel consumption and emissions, and to decrease vehicle GHG emissions. Indeed, some potential students and researchers are being dissuaded from seeking careers in IC engine research due to disparaging statements made in the popular press and elsewhere that disproportionately blame IC engines for increasing atmospheric GHGs. Without a continuous influx of enthusiastic, welltrained engineers into the profession, the potential further benefits that improved IC engines can still provide will not be realized. As responsible automotive engineers and as stewards of the environment for future generations, it is up to our community to make an honest assessment of the progress made in the development of IC engines over the past century, with their almost universal adoption to meet the world’s mobility and power generation needs. Considering that the maturity of IC engine technology is something that many other technologies/possibilities do not have, we also need to assess the potential for future progress, as well as to assess the benefits offered by competitor technologies, in order to make responsible recommendations for future directions. Factors impacting that future are discussed in this editorial and include the following:

IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture

Abstract: Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems.

A Multi-Organ Nucleus Segmentation Challenge

Abstract: Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations' COCI: a multidisciplinary comparison of coverage via citations

Abstract: New sources of citation data have recently become available, such as Microsoft Academic, Dimensions, and the OpenCitations Index of CrossRef open DOI-to-DOI citations (COCI). Although these have been compared to the Web of Science (WoS), Scopus, or Google Scholar, there is no systematic evidence of their differences across subject categories. In response, this paper investigates 3,073,351 citations found by these six data sources to 2,515 English-language highly-cited documents published in 2006 from 252 subject categories, expanding and updating the largest previous study. Google Scholar found 88% of all citations, many of which were not found by the other sources, and nearly all citations found by the remaining sources (89%-94%). A similar pattern held within most subject categories. Microsoft Academic is the second largest overall (60% of all citations), including 82% of Scopus citations and 86% of Web of Science citations. In most categories, Microsoft Academic found more citations than Scopus and WoS (182 and 223 subject categories, respectively), but had coverage gaps in some areas, such as Physics and some Humanities categories. After Scopus, Dimensions is fourth largest (54% of all citations), including 84% of Scopus citations and 88% of WoS citations. It found more citations than Scopus in 36 categories, more than WoS in 185, and displays some coverage gaps, especially in the Humanities. Following WoS, COCI is the smallest, with 28% of all citations. Google Scholar is still the most comprehensive source. In many subject categories Microsoft Academic and Dimensions are good alternatives to Scopus and WoS in terms of coverage.

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Abstract: Importance Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists’ specificity with radiologists’ sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists’ recall assessment was developed and evaluated. Results Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists’ sensitivity, lower than community-practice radiologists’ specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation.

White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group

Abstract: Alterations in white matter (WM) microstructure have been implicated in the pathophysiology of major depressive disorder (MDD). However, previous findings have been inconsistent, partially due to low statistical power and the heterogeneity of depression. In the largest multi-site study to date, we examined WM anisotropy and diffusivity in 1305 MDD patients and 1602 healthy controls (age range 12-88 years) from 20 samples worldwide, which included both adults and adolescents, within the MDD Working Group of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium. Processing of diffusion tensor imaging (DTI) data and statistical analyses were harmonized across sites and effects were meta-analyzed across studies. We observed subtle, but widespread, lower fractional anisotropy (FA) in adult MDD patients compared with controls in 16 out of 25 WM tracts of interest (Cohen's d between 0.12 and 0.26). The largest differences were observed in the corpus callosum and corona radiata. Widespread higher radial diffusivity (RD) was also observed (all Cohen's d between 0.12 and 0.18). Findings appeared to be driven by patients with recurrent MDD and an adult age of onset of depression. White matter microstructural differences in a smaller sample of adolescent MDD patients and controls did not survive correction for multiple testing. In this coordinated and harmonized multisite DTI study, we showed subtle, but widespread differences in WM microstructure in adult MDD, which may suggest structural disconnectivity in MDD.

Anthoceros genomes illuminate the origin of land plants and the unique biology of hornworts

Abstract: Hornworts comprise a bryophyte lineage that diverged from other extant land plants >400 million years ago and bears unique biological features, including a distinct sporophyte architecture, cyanobacterial symbiosis and a pyrenoid-based carbon-concentrating mechanism (CCM). Here, we provide three high-quality genomes of Anthoceros hornworts. Phylogenomic analyses place hornworts as a sister clade to liverworts plus mosses with high support. The Anthoceros genomes lack repeat-dense centromeres as well as whole-genome duplication, and contain a limited transcription factor repertoire. Several genes involved in angiosperm meristem and stomatal function are conserved in Anthoceros and upregulated during sporophyte development, suggesting possible homologies at the genetic level. We identified candidate genes involved in cyanobacterial symbiosis and found that LCIB, a Chlamydomonas CCM gene, is present in hornworts but absent in other plant lineages, implying a possible conserved role in CCM function. We anticipate that these hornwort genomes will serve as essential references for future hornwort research and comparative studies across land plants.

Fiber Bragg Gratings for Medical Applications and Future Challenges: A Review

Abstract: In the last decades, fiber Bragg gratings (FBGs) have become increasingly attractive to medical applications due to their unique properties such as small size, biocompatibility, immunity to electromagnetic interferences, high sensitivity and multiplexing capability. FBGs have been employed in the development of surgical tools, assistive devices, wearables, and biosensors, showing great potentialities for medical uses. This paper reviews the FBG-based measuring systems, their principle of work, and their applications in medicine and healthcare. Particular attention is given to sensing solutions for biomechanics, minimally invasive surgery, physiological monitoring, and medical biosensing. Strengths, weaknesses, open challenges, and future trends are also discussed to highlight how FBGs can meet the demands of next-generation medical devices and healthcare system.

Applications of Zeolites to C1 Chemistry: Recent Advances, Challenges, and Opportunities

Abstract: C1 chemistry, which is the catalytic transformation of C1 molecules including CO, CO2 , CH4 , CH3 OH, and HCOOH, plays an important role in providing energy and chemical supplies while meeting environmental requirements. Zeolites are highly efficient solid catalysts used in the chemical industry. The design and development of zeolite-based mono-, bi-, and multifunctional catalysts has led to a booming application of zeolite-based catalysts to C1 chemistry. Combining the advantages of zeolites and metallic catalytic species has promoted the catalytic production of various hydrocarbons (e.g., methane, light olefins, aromatics, and liquid fuels) and oxygenates (e.g., methanol, dimethyl ether, formic acid, and higher alcohols) from C1 molecules. The key zeolite descriptors that influence catalytic performance, such as framework topologies, nanoconfinement effects, Bronsted acidities, secondary-pore systems, particle sizes, extraframework cations and atoms, hydrophobicity and hydrophilicity, and proximity between acid and metallic sites are discussed to provide a deep understanding of the significance of zeolites to C1 chemistry. An outlook regarding challenges and opportunities for the conversion of C1 resources using zeolite-based catalysts to meet emerging energy and environmental demands is also presented.

Metal organic frameworks for biomass conversion

Abstract: Biomass is increasingly used as a source of fuels and chemicals as a renewable alternative to fossil feedstocks. Cellulose, hemicellulose and lignin are converted into platform chemicals from which a large range of compounds are derived with different structures. These biomass transformation processes require the use of efficient and durable catalysts that should drive the selectivity of the process. This review focuses on the use of metal-organic frameworks (MOFs) and derivatives as catalysts for biomass conversion. After an introduction setting up the importance of the field and the MOF features that justify their prevalence as heterogeneous catalysts for liquid phase reactions, the two main parts of the review are the description of MOF synthesis and adaptation and coverage of the catalytic reactions involving biomass substrates organized according to the type of MOF. The last section summarizes the current state of the art and our outlook for the future development of the field.

Galacto-conjugation of Navitoclax as an efficient strategy to increase senolytic specificity and reduce platelet toxicity.

Abstract: Pharmacologically active compounds with preferential cytotoxic activity for senescent cells, known as senolytics, can ameliorate or even revert pathological manifestations of senescence in numerous preclinical mouse disease models, including cancer models. However, translation of senolytic therapies to human disease is hampered by their suboptimal specificity for senescent cells and important toxicities that narrow their therapeutic windows. We have previously shown that the high levels of senescence-associated lysosomal β-galactosidase (SA-β-gal) found within senescent cells can be exploited to specifically release tracers and cytotoxic cargoes from galactose-encapsulated nanoparticles within these cells. Here, we show that galacto-conjugation of the BCL-2 family inhibitor Navitoclax results in a potent senolytic prodrug (Nav-Gal), that can be preferentially activated by SA-β-gal activity in a wide range of cell types. Nav-Gal selectively induces senescent cell apoptosis and has a higher senolytic index than Navitoclax (through reduced activation in nonsenescent cells). Nav-Gal enhances the cytotoxicity of standard senescence-inducing chemotherapy (cisplatin) in human A549 lung cancer cells. Concomitant treatment with cisplatin and Nav-Gal in vivo results in the eradication of senescent lung cancer cells and significantly reduces tumour growth. Importantly, galacto-conjugation reduces Navitoclax-induced platelet apoptosis in human and murine blood samples treated ex vivo, and thrombocytopenia at therapeutically effective concentrations in murine lung cancer models. Taken together, we provide a potentially versatile strategy for generating effective senolytic prodrugs with reduced toxicities.

Evolution of Plant Hormone Response Pathways

Abstract: This review focuses on the evolution of plant hormone signaling pathways. Like the chemical nature of the hormones themselves, the signaling pathways are diverse. Therefore, we focus on a group of hormones whose primary perception mechanism involves an Skp1/Cullin/F-box-type ubiquitin ligase: auxin, jasmonic acid, gibberellic acid, and strigolactone. We begin with a comparison of the core signaling pathways of these four hormones, which have been established through studies conducted in model organisms in the Angiosperms. With the advent of next-generation sequencing and advanced tools for genetic manipulation, the door to understanding the origins of hormone signaling mechanisms in plants beyond these few model systems has opened. For example, in-depth phylogenetic analyses of hormone signaling components are now being complemented by genetic studies in early diverging land plants. Here we discuss recent investigations of how basal land plants make and sense hormones. Finally, we propose connections between the emergence of hormone signaling complexity and major developmental transitions in plant evolution.

Neuromarketing in Haute Cuisine Gastronomic Experiences

Abstract: Gastronomic experiences offer a set of stimuli that affect the customer's perception of chef-designed food. This empirical study aims to analyze the influence on the consumer, at a cerebral level, of the stimuli characteristic of a high-level gastronomic experience, in a Michelin starred restaurant. The presentation by the waiter or chef, the plate design, the dish served, the taste of food, interaction or moment in which the food is served are the variables analyzed. Through the use of neuromarketing techniques - galvanic skin response to register emotional arousal, eye tracking to identify where consumers look, and electroencephalography to interpret emotional reactions - combined with qualitative research technique (In-depth interviews with all consumers), in order to know the natural and suggested memories, the objective of this research is to determine the emotional impact of the variables analyzed against the actual taste of food, obtaining conclusions about each variable in overall experience and allowing the authors to propose a model of order design of dishes, designed by the chef, based on emotions and thereby achieving greater efficiency in results of the experience and the memory of it. Results indicate a favorable influence on emotions when the chef presents the food. Likewise, dishes with special presentation have a greater influence at the level of interest than conventional dishes. It is important to highlight that the levels of emotion and attention fall after the midway point of the experience, due to the duration of the experience. Therefore, the dishes do not have the same emotional impact, despite being as special as at the beginning of the experience.

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

Abstract: A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research.

The future of sleep health: a data-driven revolution in sleep science and medicine.

Abstract: In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human–computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

An Emotional Analysis of False Information in Social Media and News Articles

Abstract: Fake news is risky, since it has been created to manipulate readers’ opinions and beliefs. In this work, we compared the language of false news to the real one of real news from an emotional perspective, considering a set of false information types (propaganda, hoax, clickbait, and satire) from social media and online news article sources. Our experiments showed that false information has different emotional patterns in each of its types, and emotions play a key role in deceiving the reader. Based on that, we proposed an LSTM neural network model that is emotionally infused to detect false news.

Structural modulation and direct measurement of subnanometric bimetallic PtSn clusters confined in zeolites

Abstract: This work was supported by the European Union through the European Research Council (grant ERC-AdG-2014-671093, SynCatMatch) and the Spanish government through the "Severo Ochoa Program" (SEV-2016-0683). L.L. thanks the ITQ for providing a contract. The authors also thank the Microscopy Service of the UPV for the TEM and STEM measurements. The XAS measurements were carried out in the CLAESS beamline of the ALBA synchrotron. We thank Giovanni Agostini for his kind support in the analysis of XAS data. HR-HAADF-STEM measurements were performed at DME-UCA at Cadiz University with financial support from FEDER/MINECO (MAT2017-87579-R and MAT2016-81118-P). C.W.L. thanks CAPES (Science without Frontiers -Process no. 13191/13-6) for a predoctoral fellowship. The financial support from ExxonMobil for this project is also greatly acknowledged.

Customer relationship management: digital transformation and sustainable business model innovation

Abstract: The point of departure for this study is the understanding of customer relationship management (CRM) as a set of technological solutions key for efficient business management, the benefits of which...

Evolution of Isolated Atoms and Clusters in Catalysis

Abstract: Structural transformation and evolution of active metal sites can occur in metal-catalyzed reactions in both homogeneous and heterogeneous systems. Such structural changes have an important impact on the catalytic behavior, including activity, selectivity, and stability. Aiming to establish a link between homogeneous and heterogeneous catalytic systems, this review begins with a discussion on dynamic structural transformations of metal catalysts in homogeneous reactions and the corresponding implications. We then discuss the evolution of isolated metal atoms and clusters in heterogeneous catalysts during catalyst activation and under reaction conditions. Finally, strategies for stabilizing subnanometric metal species on solid supports are presented for potential industrial applications.

Search and Rescue optimization algorithm: a new optimization method for solving constrained engineering optimization problems

Abstract: A new optimization method namely the Search and Rescue optimization algorithm (SAR) is presented here to solve constrained engineering optimization problems. This metaheuristic algorithm imitates the explorations behavior of humans during search and rescue operations. The e-constrained method is utilized as a constraint-handling technique. Besides, a restart strategy is proposed to avoid local infeasible minima in some complex constrained optimization problems. SAR is applied to solve 18 benchmark constraint functions presented in CEC 2010, 13 benchmark constraint functions, and 7 constrained engineering design problems reported in the specialized literature. The performance of SAR is compared with some state-of-the-art optimization algorithms. According to the statistical comparison results, the performance of SAR is better or highly competitive against the compared algorithms on most of the studied problems.

A dual function of SnRK2 kinases in the regulation of SnRK1 and plant growth

Abstract: Adverse environmental conditions trigger responses in plants that promote stress tolerance and survival at the expense of growth1. However, little is known of how stress signalling pathways interact with each other and with growth regulatory components to balance growth and stress responses. Here, we show that plant growth is largely regulated by the interplay between the evolutionarily conserved energy-sensing SNF1-related protein kinase 1 (SnRK1) protein kinase and the abscisic acid (ABA) phytohormone pathway. While SnRK2 kinases are main drivers of ABA-triggered stress responses, we uncover an unexpected growth-promoting function of these kinases in the absence of ABA as repressors of SnRK1. Sequestration of SnRK1 by SnRK2-containing complexes inhibits SnRK1 signalling, thereby allowing target of rapamycin (TOR) activity and growth under optimal conditions. On the other hand, these complexes are essential for releasing and activating SnRK1 in response to ABA, leading to the inhibition of TOR and growth under stress. This dual regulation of SnRK1 by SnRK2 kinases couples growth control with environmental factors typical for the terrestrial habitat and is likely to have been critical for the water-to-land transition of plants. The authors characterize a cross-talk between ABA and energy pathways. Essential ABA signalling components SnRK2s sequester SnRK1 in a protein complex, decreasing its interaction with the TOR kinase central for energy signalling.

Emotion Recognition in Immersive Virtual Reality: From Statistics to Affective Computing.

Abstract: Emotions play a critical role in our daily lives, so the understanding and recognition of emotional responses is crucial for human research. Affective computing research has mostly used non-immersive two-dimensional (2D) images or videos to elicit emotional states. However, immersive virtual reality, which allows researchers to simulate environments in controlled laboratory conditions with high levels of sense of presence and interactivity, is becoming more popular in emotion research. Moreover, its synergy with implicit measurements and machine-learning techniques has the potential to impact transversely in many research areas, opening new opportunities for the scientific community. This paper presents a systematic review of the emotion recognition research undertaken with physiological and behavioural measures using head-mounted displays as elicitation devices. The results highlight the evolution of the field, give a clear perspective using aggregated analysis, reveal the current open issues and provide guidelines for future research.