A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems

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

Towards multimodal driver state monitoring systems for highly automated driving

Real-time monitoring of drivers’ functional states will soon become a required safety feature for commercially available vehicles with automated driving capability. Automated driving technology aims to mitigate human error from road transport with the progressive automatisation of specific driving tasks. However, while control of the driving task remains shared between humans and automated systems, the inclusion of this new technology is not exempt from other human factors-related challenges. Drivers’ functional states are essentially a combination of psychological, emotional, and cognitive states, and they generate a constant activity footprint available for measurement through neural and peripheral physiology, among other measures. These factors can determine drivers’ functional states and, thus, drivers’ availability to safely perform control transitions between human and vehicle. This doctoral project aims at investigating the potential of electrocardiogram (ECG), electrodermal activity (EDA) and functional near-infrared spectroscopy (fNIRS) as measures for a multimodal driver state monitoring (DSM) system for highly automated driving (i.e., SAE levels 3 and 4). While current DSM systems relying on gaze behaviour measures have proven valid and effective, several limitations and challenges could only be overcome using eye-tracking in tandem with physiological parameters. This thesis investigates whether ECG, EDA and fNIRS would be good candidates for such a purpose. Two driving simulator studies were performed to measure mental workload, trust in automation, stress and perceived risk, all identified as modulators of drivers’ functional states and that could eventually determine drivers’ availability to take-over manual control. The main findings demonstrate that DSM systems should adopt multiple physiological measures to capture changes in functional states relevant for driver readiness. Future DSM systems will benefit from the knowledge generated by this research by applying machine learning methods to these measures for determining drivers’ availability for optimal take-over performance

Inferring implicit relevance from physiological signals

Ongoing growth in data availability and consumption has meant users are increasingly faced with the challenge of distilling relevant information from an abundance of noise. Overcoming this information overload can be particularly difficult in situations such as intelligence analysis, which involves subjectivity, ambiguity, or risky social implications. Highly automated solutions are often inadequate, therefore new methods are needed for augmenting existing analysis techniques to support user decision making. This project investigated the potential for deep learning to infer the occurrence of implicit relevance assessments from users' biometrics. Internal cognitive processes manifest involuntarily within physiological signals, and are often accompanied by 'gut feelings' of intuition. Quantifying unconscious mental processes during relevance appraisal may be a useful tool during decision making by offering an element of objectivity to an inherently subjective situation. Advances in wearable or non-contact sensors have made recording these signals more accessible, whilst advances in artificial intelligence and deep learning have enhanced the discovery of latent patterns within complex data. Together, these techniques might make it possible to transform tacit knowledge into codified knowledge which can be shared. A series of user studies recorded eye gaze movements, pupillary responses, electrodermal activity, heart rate variability, and skin temperature data from participants as they completed a binary relevance assessment task. Participants were asked to explicitly identify which of 40 short-text documents were relevant to an assigned topic. Investigations found this physiological data to contain detectable cues corresponding with relevance judgements. Random forests and artificial neural networks trained on features derived from the signals were able to produce inferences with moderate correlations with the participants' explicit relevance decisions. Several deep learning algorithms trained on the entire physiological time series data were generally unable to surpass the performance of feature-based methods, and instead produced inferences with low correlations with participants' explicit personal truths. Overall, pupillary responses, eye gaze movements, and electrodermal activity offered the most discriminative power, with additional physiological data providing diminishing or adverse returns. Finally, a conceptual design for a decision support system is used to discuss social implications and practicalities of quantifying implicit relevance using deep learning techniques. Potential benefits included assisting with introspection and collaborative assessment, however quantifying intrinsically unknowable concepts using personal data and abstruse artificial intelligence techniques were argued to pose incommensurate risks and challenges. Deep learning techniques therefore have the potential for inferring implicit relevance in information-rich environments, but are not yet fit for purpose. Several avenues worthy of further research are outlined

Contributions to physiological computing by means of automatic learning.

169 p.El trabajo presentado en esta tesis se enmarca dentro de dos áreas dentro de la computación fisiológica, que a su vez forma parte de las ciencias de la computación. La primera área trabajada corresponde a la de la detección de fenómenos psicológicos y estados mentales mediante la monitorización de las variables fisiológicas de las personas. La segunda área que se estudia en esta tesis forma parte del estudio de formas alternativas de interacción: los interfaces cerebro-computador.La primera contribución mejora un sistema de lógica difusa que, mediante la monitorización de las señales fisiológicas, es capaz de dar una estimación continuada en el tiempo del nivel del estrés mental. La segunda contribución continua con esta línea y estudia la detección de las respuestas fisiológicas del fenómeno opuesto al estrés: la relajación. En esta contribución se presentan características innovadoras que facilitan dicha detección y la pone en práctica con métodos de aprendizaje automático.Finalmente, la tercera contribución estudia diferentes técnicas de aprendizaje para distinguir entre cuatro clases de movimiento más una quinta clase de no intencionalidad de movimiento en un problema de BCI