Over recent years, robots are increasingly being employed in several aspects of modern society. Among others, social robots have the potential to benefit education, healthcare, and tourism. To achieve this purpose, robots should be able to engage humans, recognize users’ emotions, and to some extent properly react and "behave" in a natural interaction. Most robotics applications primarily use visual information for emotion recognition, which is often based on facial expressions. However, the display of emotional states through facial expression is inherently a voluntary controlled process that is typical of human–human interaction. In fact, humans have not yet learned to use this channel when communicating with a robotic technology. Hence, there is an urgent need to exploit emotion information channels not directly controlled by humans, such as those that can be ascribed to physiological modulations. Thermal infrared imaging-based affective computing has the potential to be the solution to such an issue. It is a validated technology that allows the non-obtrusive monitoring of physiological parameters and from which it might be possible to infer affective states. This review is aimed to outline the advantages and the current research challenges of thermal imaging-based affective computing for human–robot interaction.

Thermal infrared imaging has been proposed, and is now used, as a tool for the non-contact and non-invasive computational assessment of human autonomic nervous activity and psychophysiological states. Thanks to a new generation of high sensitivity infrared thermal detectors and the development of computational models of the autonomic control of the facial cutaneous temperature, several autonomic variables can be computed through thermal infrared imaging, including localized blood perfusion rate, cardiac pulse rate, breath rate, sudomotor and stress responses. In fact, all of these parameters impact on the control of the cutaneous temperature. The physiological information obtained through this approach, could then be used to infer about a variety of psychophysiological or emotional states, as proved by the increasing number of psychophysiology or neurosciences studies that use thermal infrared imaging. This paper presents a review of the principal achievements of thermal infrared imaging in computational psychophysiology, focusing on the capability of the technique for providing ubiquitous and unwired monitoring of psychophysiological activity and affective states. It also presents a summary on the modern, up-to-date infrared sensors technology.

Functional infrared imaging was used to study the facial thermal signatures of three fundamental emotional conditions: stress, fear and pleasure arousal. Facial cutaneous temperature and its topographic distribution exhibited specific features clearly correlated to emotional arousal and concomitant measures of standard physiological signals of the sympathetic activity. The results of this study indicate functional infrared imaging as an alternative, touch less, non invasive method for assessing individual's emotional arousal in psychophysiology.

Infrared thermal imaging (IRI) is a contact-less technology able to monitor human skin temperature for biomedical applications and in real-life contexts. Its capacity to detect fever was exploited for mass screening during past epidemic emergencies as well as for the current COVID-19 pandemic. However, the only assessment of fever may not be selective for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Hence, novel approaches for IRI data analysis have been investigated. The present review aims to describe how IRI have been employed during the last epidemics, highlighting the potentialities and the limitations of this technology to contain the contagions. Specifically, the methods employed for automatic face recognition and fever assessment and IRI’s performances in mass screening at airports and hospitals are reviewed. Moreover, an overview of novel machine learning methods for IRI data analysis, aimed to identify respiratory diseases, is provided. In addition, IRI-based smart technologies developed to support the healthcare during the COVID-19 pandemic are described. Finally, relevant guidelines to fully exploit IRI for COVID-19 identification are defined, to improve the effectiveness of IRI in the detection of the SARS-CoV-2 infection.

Objective: Interpretation of the Electroencephalographic (EEG) and Magnetoencephalographic (MEG) signals requires off-line artifacts removal. Since artifacts share frequencies with brain activity, filtering is insufficient. Blind source separation, mainly through Independent Component Analysis (ICA), is the gold-standard procedure for the identification of artifacts in multi-dimensional recordings. However, a classification of brain and artifactual Independent Components (ICs) is still required. Since ICs exhibit recognizable patterns, classification is usually performed by experts' visual inspection. This procedure is time-consuming and prone to errors. Automatic ICs classification has been explored, often through complex ICs features extraction prior to classification. Relying on Deep Learning ability of self-extracting the features of interest, we investigated the capabilities of Convolutional Neural Networks (CNNs) for off-line, automatic artifact identification through ICs without feature selection. Methods: A CNN was applied to spectrum and topography of a large data-set of few thousand samples of ICs obtained from multi-channel EEG and MEG recordings acquired during heterogeneous experimental settings and on different subjects. CNN performances, when applied to EEG, MEG, and combined EEG and MEG ICs, were explored and compared with state of the art feature-based automatic classification. Results: Beyond state-of-the-art automatic classification accuracies were demonstrated through cross-validation (92.4% EEG, 95.4% MEG, 95.6% EEG+MEG). Conclusion: High CNN classification performances were achieved through heuristically selection of machinery hyperparameters and through the CNN self-selection of the features of interest. Significance: Considering the large data availability of multi-channel EEG and MEG recordings, CNNs may be suited for classification of ICs of multi-channel brain electrophysiological recordings.