A Quantum NeuroIS data analytics architecture for the measurement of computer anxiety: a tool for the usability evaluation of learning management systems.

NeuroIS uses neurotechnology tools such as Electroencephalogram (EEG) that can be used to measure high brainwave frequencies that can be linked to human anxiety. Past research showed that computer anxiety influences how users perceive ease of use of a learning management system (LMS). Although computer anxiety has been used successfully to evaluate the usability of LMS, the main data collection mechanisms proposed for its evaluation has been questionnaires. Questionnaires suffer from possible problems such inadequate to understand some forms of information such as emotions, lacks validity, possible lack of thought and honesty in the responses. Quantum based approaches to consciousness have been very popular in the last years including the quantum model reduction in microtubules of Penrose & Hameroff, (1995), where quantum coherence occurs by exciting quasicrystalline water molecules as dipoles buried in microtubules. Quantum consciousness models measure changes in states of consciousness that can help to identify usability issues in computer systems. The objective of the chapter is to propose an architecture based on a NeuroIS that collects data by using EEG from users and then use the collected data to perform analytics by using a quantum consciousness model proposed for computer anxiety measurements that can be used for the usability testing of a LMS

Applications of Affective Computing in Human-Robot Interaction: state-of-art and challenges for manufacturing

The introduction of collaborative robots aims to make production more flexible, promoting a greater interaction between humans and robots also from physical point of view. However, working closely with a robot may lead to the creation of stressful situations for the operator, which can negatively affect task performance. In Human-Robot Interaction (HRI), robots are expected to be socially intelligent, i.e., capable of understanding and reacting accordingly to human social and affective clues. This ability can be exploited implementing affective computing, which concerns the development of systems able to recognize, interpret, process, and simulate human affects. Social intelligence is essential for robots to establish a natural interaction with people in several contexts, including the manufacturing sector with the emergence of Industry 5.0. In order to take full advantage of the human-robot collaboration, the robotic system should be able to perceive the psycho-emotional and mental state of the operator through different sensing modalities (e.g., facial expressions, body language, voice, or physiological signals) and to adapt its behaviour accordingly. The development of socially intelligent collaborative robots in the manufacturing sector can lead to a symbiotic human-robot collaboration, arising several research challenges that still need to be addressed. The goals of this paper are the following: (i) providing an overview of affective computing implementation in HRI; (ii) analyzing the state-of-art on this topic in different application contexts (e.g., healthcare, service applications, and manufacturing); (iii) highlighting research challenges for the manufacturing sector

Patient centric intervention for children with high functioning autism spectrum disorder. Can ICT solutions improve the state of the art ?

In my PhD research we developed an integrated technological platform for the acquisition of neurophysiologic signals in a semi-naturalistic setting where children are free to move around, play with different objects and interact with the examiner. The interaction with the examiner rather than with a screen is another very important feature of the present research, and allows recreating a more real situation with social interactions and cues. In this paradigm, we can assume that the signals acquired from the brain and the autonomic system, are much more similar to what is generated while the child interacts in common life situations. This setting, with a relatively simple technical implementation, can be considered as one step towards a more behaviorally driven analysis of neurophysiologic activity. Within the context of a pilot open trial, we showed the feasibility of the technological platform applied to the classical intervention solutions for the autism. We found that (1) the platform was useful during both children-therapist interaction at hospital as well as children-parents interaction at home, (2) tailored intervention was compatible with at home use and non-professional therapist/parents. Going back to the title of my thesis: 'Can ICT solution improve the state-of-the-art ?' the answer could be: 'Yes it can be an useful support for a skilled professional in the field of autis

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

Public policy, social marketing and neuromarketing: from addressing the consumer behaviour to addressing the social behaviour - a study on the assessment of Public Service Announcements’ efficacy by neuro-metric indexes and techniques

The overall aim of this thesis is to investigate to what extent marketing can be a useful science for the public policy in developing effective Public Service Announcements (PSAs). In particular, hereby a specific discipline will be taken in consideration: the one that merges marketing with neuroscience, that is the so-called ‘neuromarketing’, which - in order to assess the advertising efficacy - adopts biometric and neurometric indexes. The objective of this work is to gain insights into the above-mentioned fields (marketing, neuroscience and public policy) by: - reviewing previous studies, as well as topical literature; - exploring the latest case studies and best practises; - examining the traditional methods’ results for the assessment of the PSAs (i.e. polls, surveys, focus groups) in their evolutionary path (till arriving to birth of the the neurometric methods) Such kind of research has the purpose to identify the factors that are considered relevant to answer the ultimate research question: is it possible today, by using state-of-the-art neurometric indexes and techniques, to provide policymakers with precise guidelines for developing effective PSAs, so that marketing will be able to address no more just the consumer behaviour, but also the social behaviour? In fact, the goal of any advertising campaign is to convey a specific message and reach a specific audience: the consumers. But, when talking about PSAs, many things changes: the KPIs for the assessment of their efficacy are no longer the commercial ones (GRP, reach etc.), but rather the gain obtained in public health after the airing of the campaign. Consequently, the specific message will be a different ‘call-to-action’: no more an invite to purchase, but rather to change a (wrong) social behaviour or adopt a (right) civil conscience. Given these premises, it is possible that marketing could be invested with a precise responsibility in terms of lives saved and public health. The practical and managerial implications of the research are the following: EU policymakers and local governments will have the opportunity to dispose of scientific data and information about the society that might be transformed in guidelines for producing effective PSAs based on the inner audience’s insights. The originality of this research resides in having framed the new neuromarketing protocols in the traditional Consumer Behaviour theory, combining thus future and past of the marketing research

Measuring Behavior 2018 Conference Proceedings

These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions