Measuring stress and cognitive load effects on the perceived quality of a multimodal dialogue system

In this paper we present the results of a pilot study investigating the impact of stress and cognitive load on the perceived interaction quality of a multimodal dialogue system for crisis management. Four test subjects interacted with the system in four differently configured trials aiming to induce low/high levels of stress and cognitive load. To measure the level of stress and cognitive load physiological sensors and subjective ratings were collected. After each trial the subjects filled in an evaluation questionnaire regarding the system interaction quality. In the end we conducted an in-depth interview with each subject. The trials were recorded with a webcam to facilitate the behaviour analysis. Results showed that both factors have an influence on the way subjects perceived the interaction quality, whereas the cognitive load seems to have a higher impact. Further quantitative experiments are needed in order to validate the results and quantify the weight of each factor. \u

Smart City Technologies: Design and Evaluation of An Intelligent Driving Assistant for Smart Parking

Smart cities technologies are gradually changing our urban landscape thanks to the proliferation of billions of smart devices permanently connected through the internet. Among technologies with highest impact on citizen’s quality of life are intelligent transportation systems and in particular, smart parking applications. In this paper, we present a study evaluation the design of a smart parking assistant developed in our lab. The system is implemented as mobile app with an integrated GUI adapted for Android tablets. The app extends common park guidance information systems (PGI) offering suggestions based on parking fee or proximity to destination. Two novel features – beyond the state of the art of current available systems – are added: the use of natural language and the ability to react in real-time to changes in parking occupancy. If the number of parking lots drops to critical level, the application redirects the driver to another parking place. Furthermore, the app includes GPS and Google maps interfacing modules which enable the application to detect the driver location and calculate the nearest car park distance. A group of five experts with background in interface design and natural language processing evaluated the prototype using Nielsen’s set of heuristics in a think-loud approach. Results and implications for further interaction design are extensively discussed

An ambient agent model for reading companion robot

Reading is essentially a problem-solving task. Based on what is read, like problem solving, it requires effort, planning, self-monitoring, strategy selection, and reflection. Also, as readers are trying to solve difficult problems, reading materials become more complex, thus demands more effort and challenges cognition. To address this issue, companion robots can be deployed to assist readers in solving difficult reading tasks by making reading process more enjoyable and meaningful. These robots require an ambient agent model, monitoring of a reader’s cognitive demand as it could consist of more complex tasks and dynamic interactions between human and environment. Current cognitive load models are not developed in a form to have reasoning qualities and not integrated into companion robots. Thus, this study has been conducted to develop an ambient agent model of cognitive load and reading performance to be integrated into a reading companion robot. The research activities were based on Design Science Research Process, Agent-Based Modelling, and Ambient Agent Framework. The proposed model was evaluated through a series of verification and validation approaches. The verification process includes equilibria evaluation and automated trace analysis approaches to ensure the model exhibits realistic behaviours and in accordance to related empirical data and literature. On the other hand, validation process that involved human experiment proved that a reading companion robot was able to reduce cognitive load during demanding reading tasks. Moreover, experiments results indicated that the integration of an ambient agent model into a reading companion robot enabled the robot to be perceived as a social, intelligent, useful, and motivational digital side-kick. The study contribution makes it feasible for new endeavours that aim at designing ambient applications based on human’s physical and cognitive process as an ambient agent model of cognitive load and reading performance was developed. Furthermore, it also helps in designing more realistic reading companion robots in the future

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