Non-Intrusive Affective Assessment in the Circumplex Model from Pupil Diameter and Facial Expression Monitoring

Automatic methods for affective assessment seek to enable computer systems to recognize the affective state of their users. This dissertation proposes a system that uses non-intrusive measurements of the user’s pupil diameter and facial expression to characterize his /her affective state in the Circumplex Model of Affect. This affective characterization is achieved by estimating the affective arousal and valence of the user’s affective state. In the proposed system the pupil diameter signal is obtained from a desktop eye gaze tracker, while the face expression components, called Facial Animation Parameters (FAPs) are obtained from a Microsoft Kinect module, which also captures the face surface as a cloud of points. Both types of data are recorded 10 times per second. This dissertation implemented pre-processing methods and fixture extraction approaches that yield a reduced number of features representative of discrete 10-second recordings, to estimate the level of affective arousal and the type of affective valence experienced by the user in those intervals. The dissertation uses a machine learning approach, specifically Support Vector Machines (SVMs), to act as a model that will yield estimations of valence and arousal from the features derived from the data recorded. Pupil diameter and facial expression recordings were collected from 50 subjects who volunteered to participate in an FIU IRB-approved experiment to capture their reactions to the presentation of 70 pictures from the International Affective Picture System (IAPS) database, which have been used in large calibration studies and therefore have associated arousal and valence mean values. Additionally, each of the 50 volunteers in the data collection experiment provided their own subjective assessment of the levels of arousal and valence elicited in him / her by each picture. This process resulted in a set of face and pupil data records, along with the expected reaction levels of arousal and valence, i.e., the “labels”, for the data used to train and test the SVM classifiers. The trained SVM classifiers achieved 75% accuracy for valence estimation and 92% accuracy in arousal estimation, confirming the initial viability of non-intrusive affective assessment systems based on pupil diameter and face expression monitoring

Physiological Sensing for Affective Computing

This thesis addresses two aspects related to enabling systems to recognize the affective state of people and respond sensibly to it. First, the issue of representing affective states and unambiguously assigning physiological measurements to those is addressed by suggesting a new approach based on the dimensional emotion model of valence and arousal. Second, the issue of sensing affect-related physiological data is addressed by suggesting a concept for physiological sensor systems that live up to the requirements of adaptive, user-centred systems.In dieser Arbeit wird ein Konzept zur eindeutigen Zuordnung physiologischer Messdaten zu Emotionszuständen erarbeitet, wobei Probleme klassischer Ansätze hierzu vermieden werden. Des Weiteren widmet sich die Arbeit der Erfassung emotionsbezogener physiologischer Parameter. Es wird ein Konzept für Sensorsysteme vorgestellt, welches die zuverlässige Erfassung relevanter physiologischer Parameter erlaubt, ohne jedoch den Nutzer stark zu beeinträchtigen. Der Schwerpunkt liegt hierbei auf der alltagstauglichen Gestaltung des Systems

Utilising Emotion Monitoring for Developing Music Interventions for People with Dementia:A State-of-the-Art Review

The demand for smart solutions to support people with dementia (PwD) is increasing. These solutions are expected to assist PwD with their emotional, physical, and social well-being. At the moment, state-of-the-art works allow for the monitoring of physical well-being; however, not much attention is delineated for monitoring the emotional and social well-being of PwD. Research on emotion monitoring can be combined with research on the effects of music on PwD given its promising effects. More specifically, knowledge of the emotional state allows for music intervention to alleviate negative emotions by eliciting positive emotions in PwD. In this direction, the paper conducts a state-of-the-art review on two aspects: (i) the effect of music on PwD and (ii) both wearable and non-wearable sensing systems for emotional state monitoring. After outlining the application of musical interventions for PwD, including emotion monitoring sensors and algorithms, multiple challenges are identified. The main findings include a need for rigorous research approaches for the development of adaptable solutions that can tackle dynamic changes caused by the diminishing cognitive abilities of PwD with a focus on privacy and adoption aspects. By addressing these requirements, advancements can be made in harnessing music and emotion monitoring for PwD, thereby facilitating the creation of more resilient and scalable solutions to aid caregivers and PwD

FusionSense: Emotion Classification using Feature Fusion of Multimodal Data and Deep learning in a Brain-inspired Spiking Neural Network

Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocardiogram (EEG), electroencephalogram (ECG), skin temperature, along with facial expressions, voice, posture to name a few, in order to classify emotions. Spiking neural networks (SNNs) represent the third generation of neural networks and employ biologically plausible models of neurons. SNNs have been shown to handle Spatio-temporal data, which is essentially the nature of the data encountered in emotion recognition problem, in an efficient manner. In this work, for the first time, we propose the application of SNNs in order to solve the emotion recognition problem with the multimodal dataset. Specifically, we use the NeuCube framework, which employs an evolving SNN architecture to classify emotional valence and evaluate the performance of our approach on the MAHNOB-HCI dataset. The multimodal data used in our work consists of facial expressions along with physiological signals such as ECG, skin temperature, skin conductance, respiration signal, mouth length, and pupil size. We perform classification under the Leave-One-Subject-Out (LOSO) cross-validation mode. Our results show that the proposed approach achieves an accuracy of 73.15% for classifying binary valence when applying feature-level fusion, which is comparable to other deep learning methods. We achieve this accuracy even without using EEG, which other deep learning methods have relied on to achieve this level of accuracy. In conclusion, we have demonstrated that the SNN can be successfully used for solving the emotion recognition problem with multimodal data and also provide directions for future research utilizing SNN for Affective computing. In addition to the good accuracy, the SNN recognition system is requires incrementally trainable on new data in an adaptive way. It only one pass training, which makes it suitable for practical and on-line applications. These features are not manifested in other methods for this problem.Peer reviewe

The psychophysiology primer : A guide to methods and a broad review with a focus on human-computer interaction

Publisher Copyright: © 2016 B. Cowley, M. Filetti, K. Lukander.Peer reviewe

Towards estimating computer users' mood from interaction behaviour with keyboard and mouse

The purpose of this exploratory research was to study the relationship between the mood of computer users and their use of keyboard and mouse to examine the possibility of creating a generic or individualized mood measure. To examine this, a field study (n = 26) and a controlled study (n = 16) were conducted. In the field study, interaction data and self-reported mood measurements were collected during normal PC use over several days. In the controlled study, participants worked on a programming task while listening to high or low arousing background music. Besides subjective mood measurement, galvanic skin response (GSR) data was also collected. Results found no generic relationship between the interaction data and the mood data. However, the results of the studies found significant average correlations between mood measurement and personalized regression models based on keyboard and mouse interaction data. Together the results suggest that individualized mood prediction is possible from interaction behaviour with keyboard and mouse

Design and Development of a Real-Time Bio-Sensing System Assessing Student Mental Workload and Engagement

Ο εντοπισμός του επακριβούς επιπέδου προσήλωσης και εμπλοκής των μαθητών με το περιεχόμενο διδασκαλίας στην τάξη είναι ένας από τους πιο μεγαλεπήβολους στόχους των ερευνητών της εκπαιδευτικής και επιστημονικής κοινότητας. (Lang, 1995, Grossberg, 1987). Σχετικές διεπιστημονικές ερευνητικές προσπάθειες προσαύξησης ενδιαφέροντος και εντοπισμού της αποτελεσματικότητας των διδακτικών πρακτικών βασίζονται σε τυπικές μελέτες από τον χώρο της ψυχολογίας, της παιδαγωγικής, της παιδοψυχολογίας και της ψυχοφυσιολογίας. Νέες τεχνολογίες έχουν εισάγει διαγνωστικές συσκευές δανεισμένες από τον χώρο της ιατρικής με σκοπό να εκμεταλλευτούν τις δυνατότητες μετρήσεων βιολογικών σημάτων τα οποία αποτελούν επιβεβαιωμένες εκφράσεις ψυχοφυσιολογικών καταστάσεων οι οποίες μπορούν να μεταφραστούν σε εκδηλώσεις διέγερσης και διάθεσης. Οι ιατρικές συσκευές απαιτούν εργαστηριακό περιβάλλον λόγω των αναγκών χρήσης ηλεκτροδίων, κινητικών περιορισμών, συγχρονισμού και ομοιομορφίας των στοιχείων που προκύπτουν και γι’ αυτό τον λόγο δεν μπόρεσαν ποτέ να αποδόσουν μια προσιτή λύση εφαρμόσιμη ευρύτερα σε εκπαιδευτικό περιβάλλον. Στην παρούσα μελέτη, αναλύονται οι επιδόσεις μιας ειδικά κατασκευασμένης ηλεκτρονικής συσκευής, σχεδιασμένης ώστε να εξεταστούν οι δυνατότητες να εξαχθούν δείκτες ψυχοσωματικών εκφράσεων του χρήστη, με την δυνατότητα να χρησιμοποιείται εύχρηστα στην τάξη χωρίς ηλεκτρόδια και επηρεασμούς από προσαρτήσεις. Το ολοκληρωμένο σύστημα μέτρησης και αποτύπωσης συμπερασμάτων είναι βασισμένο σε μοντελοποίηση συμπεριφορών αλλαγής του καρδιακού παλμού και της ειδικής διηλεκτρικής αγωγιμότητας του δέρματος σε πραγματικό χρόνο. Η συσκευή χρησιμοποιεί οπτικούς και διηλεκτρικούς αισθητήρες επαφής και έχει μελετηθεί σε αντιπαραβολή με διαβαθμισμένα περιβάλλοντα προκλητών καταστάσεων νοητικής φόρτισης. Σειρές πειραματικών διαδικασιών εφαρμοσμένες σε διαβαθμισμένα σενάρια πρόκλησης ψυχοσωματικών διεγέρσεων έχουν ολοκληρωθεί για επικύρωση, μελέτη επιδόσεων και λειτουργία του συστήματος ακόμη και σε σύγκριση με εμπορικό προϊόν. Πειραματικά αποτελέσματα δείχνουν αξιόλογους συσχετισμούς του μοντέλου και των επιδόσεων του συστήματος με τις αναμενόμενες αποκρίσεις με ενθαρρυντικά ποσοστά ακρίβειας.Facing the challenge of improving adaptive interaction in educational technologies scientists and educators have turned their focal point in diverse areas ranging from educational, teaching and behavioural psychology to cognitive, affective and perceptual neuroscience. The introduction of digital technologies and interactive media tools in education has shown improved learning efficiency, much higher memory activation and assimilation than verbal teaching, notably due to enhancing motivation achieved by employing approaches attracting student’s attention. Excelling aspects of audio visual presentation proved highly valuable particularly in classes with multi ethnic groups of students, as for example consistency between definitions and objects which were verbally and visually defined, eliminating possible misconceptions caused by mishearing or misinterpretation by the learner. Taking it all one step further as to how an educational system could be even more efficient, a new element would be needed revealing a credible judgment of learning scores and effectiveness of the learning process instantaneously as for example inner levels of activation and satisfaction. In fact, this could be made possible using existing technologies if subconscious neurophysiological responses of a learner could be ascertained and inferred to psycho-somatic conditions as they occur. A system including bio-sensing, data analysis and processing in real time able to provide quantified markers of psychosomatic states of a learner would help enormously in next generations of educational practice. Incorporating data of student engagement and active involvement could help to deduce the interest of a learner, which is known to improve sensitisation in implicit, incidental and also in classical learning. Experimental settings used in previous studies attempting to incorporate physiological responses and interpretations into responsive educational settings have faced major obstacles. Operational issues caused by the requirements of the devices used for the acquisition of physiological signals such as electrodes and movement restrictions have reduced the progress of such settings to laboratory environments. In such settings as described above, the effects of wiring harnesses and sensory components produced an additional psychological burden on the participants. Consequently, the need to approach the physiological data acquisition from a new angle with seamless and unnoticeable operation is apparent. The challenge to design, develop and validate a system that being minimally obstructive and literally unnoticed by the user would uncover combined subconscious expressions of a learner was the primary objective of this research. Physiological data of Heart Rate and Skin Trans-Conductance (Electro-dermal Response) elected as vitally important and highly appropriate to produce the input of data required to evaluate a behavioural concept model. The behavioural assessment model entailed vector classifiers producing directional interpretations of measurements. Directional information (Gradient response) has been derived by comparison of measurements to previously measured values in real time. Assessing the effectiveness and accuracy of the adopted model to deduce attention and engagement of a learner in real time formed the second major objective. For this purpose, a series of relevant experimental methodologies have been employed. Data produced using formal personality assessments have also been investigated in conjunction with those derived from physiological responses in order to identify personality related particularities. The final part of this work has been supplemented by propositions and suggestions with regards to various applications of the system in accomplishment of the initial aims