A computational model of observer stress

Stress is a major growing concern in our age, adversely impacting individuals and society. Stress research has a wide range of benefits with the potential to improve health and wellbeing, personal day-to-day activities, increase work productivity and benefit society as a whole. This makes it an interesting and socially beneficial area of research. It motivates objective understanding of how average individuals respond to events they observe in typical environments they encounter, which this thesis investigates through artificial intelligence particularly bio-inspired computing and data mining. This thesis presents a review of the sensors that show symptoms which have been used to detect stress and computational modelling of stress. It discusses non-invasive and unobtrusive sensors for measuring computed stress. The focus is on sensors that do not impede everyday activities which could be used to monitor stress levels on a regular basis. Several computational techniques have been developed previously by others to model stress based on techniques including machine learning techniques but these are quite simplistic and inadequate. This thesis presents novel enhanced methods for modelling stress for classification and prediction using real-world stress data sets. The main aims for this thesis are to propose and define the concept of observer stress and develop computational models of observer stress for typical environments. The environments considered in this thesis are abstract virtual environments (text), virtual environments (films) and real environments (real-life settings). The research comprised stress data capture for the environments, multi-sensor signal processing and fusion, and knowledge discovery methods for the computational models to recognise and predict observer stress. Experiments were designed and conducted to acquire real-world observer stress data sets for the different environments. The data sets contain physiological and physical sensor signals of observers and survey reports that validate stress in the environments. The physiological stress signals in the data sets include electroencephalogram (EEG), electrocardiogram (ECG), galvanic skin response, blood pressure and the physical signals include eye gaze, pupil dilation and videos of faces in visible and thermal spectrums. Observer stress modelling systems were developed using analytics on the stress data sets. The systems generated stress features from the data and used these features to develop computational models based on techniques such as support vector machines and artificial neural networks to capture stress patterns. Some systems also optimised features using techniques such as genetic algorithm or correlation based techniques for developing models to capture better stress patterns for observer stress recognition. Additionally, a computational stress signal predictor system was developed to model temporal stress. This system was based on a novel combination of support vector machine, genetic algorithm and an artificial neural network. This thesis contributes a significant dimension to computational stress research. It investigates observer stress, proposes novel computational methods for stress, models stress with novel stress feature sets, and proposes a model for a temporal stress measure. The research outcomes provide an objective understanding on stress levels of observers, and environments based on observer perceptions. Further research suggested includes investigating models to manage stress conditions and observer behaviours

Computational Models of Stress in Reading Using Physiological and Physical Sensor Data

Stress is a major problem facing our world today and it is important to develop an objective understanding of how average individuals respond to stress in a typical activity like reading. The aim for this paper is to determine whether stress patterns ca

Eye Tracking to Support eLearning

Online eLearning environments to support student learning are of growing importance. Students are increasingly turning to online resources for education; sometimes in place of face-to-face tuition. Online eLearning extends teaching and learning from the classroom to a wider audience with different needs, backgrounds, and motivations. The one-size-fits-all approach predominately used is not effective for catering to the needs of all students. An area of the increasing diversity is the linguistic background of readers. More students are reading in their non-native language. It has previously been established that first English language (L1) students read differently to second English language (L2) students. One way of analysing this difference is by tracking the eyes of readers, which is an effective way of investigating the reading process. In this thesis we investigate the question of whether eye tracking can be used to make learning via reading more effective in eLearning environments. This question is approached from two directions; first by investigating how eye tracking can be used to adapt to individual student’s understanding and perceptions of text. The second approach is analysing a cohort’s reading behaviour to provide information to the author of the text and any related comprehension questions regarding their suitability and difficulty. To investigate these questions, two user studies were carried out to collect eye gaze data from both L1 and L2 readers. The first user study focussed on how different presentation methods of text and related questions affected not only comprehension performance but also reading behaviour and student perceptions of performance. The data from this study was used to make predictions of reading comprehension that can be used to make eLearning environments adaptive, in addition to providing implicit feedback about the difficulty of text and questions. In the second study we investigate the effects of text readability and conceptual difficulty on eye gaze, prediction of reading comprehension, and perceptions. This study showed that readability affected the eye gaze of L1 readers and conceptual difficulty affected the eye gaze of L2 readers. The prediction accuracy of comprehension was consequently increased for the L1 group by increased difficulty in readability, whereas increased difficulty in conceptual level corresponded to increased accuracy for the L2 group. Analysis of participants’ perceptions of complexity revealed that readability and conceptual difficulty interact making the two variables hard for the reader to disentangle. Further analysis of participants’ eye gaze revealed that both the predefined and perceived text complexity affected eye gaze. We therefore propose using eye gaze measures to provide feedback about the implicit reading difficulty of texts read. The results from both studies indicate that there is enormous potential in using eye tracking to make learning via reading more effective in eLearning environments. We conclude with a discussion of how these findings can be applied to improve reading within eLearning environments. We propose an adaptive eLearning architecture that dynamically presents text to students and provides information to authors to improve the quality of texts and questions