How Are We Connected? Measuring Audience Galvanic Skin Response of Connected Performances."

Accurately measuring the audience response during a performance is a difficult task. This is particularly the\ud case for connected performances. In this paper, we staged a connected performance in which a remote\ud audience enjoyed the performance in real-time. Both objective (galvanic skin response and behaviours) and\ud subjective (interviews) responses from the live and remote audience members were recorded. To capture\ud galvanic skin response, a group of self-built sensors was used to record the electrical conductance of the skin.\ud The results of the measurements showed that both the live and the remote audience members had a similar\ud response to the connected performance even though more vivid artistic artefacts had a stronger effect on the\ud live audience. Some technical issues also influenced the experience of the remote audience. In conclusion we\ud found that the remoteness had little influence on the connected performance

How are we connected?

Accurately measuring the audience response during a performance is a difficult task. This is particularly the case for connected performances. In this paper, we staged a connected performance in which a remote audience enjoyed the performance in real-time. Both objective (galvanic skin response and behaviours) and subjective (interviews) responses from the live and remote audience members were recorded. To capture galvanic skin response, a group of self-built sensors was used to record the electrical conductance of the skin. The results of the measurements showed that both the live and the remote audience members had a similar response to the connected performance even though more vivid artistic artefacts had a stronger effect on the live audience. Some technical issues also influenced the experience of the remote audience. In conclusion we found that the remoteness had little influence on the connected performance

Effect of Personality Traits on UX Evaluation Metrics: A Study on Usability Issues, Valence-Arousal and Skin Conductance

Personality affect the way someone feels or acts. This paper examines the effect of personality traits, as operationalized by the Big-five questionnaire, on the number, type, and severity of the identified usability issues, physiological signals (skin conductance), and subjective emotional ratings (valence-arousal).Twenty-four users interacted with a web service and then participated in a retrospective thinking aloud session. Results revealed that the number of usability issues is significantly affected by the Openness trait. Emotional Stability significantly affects the type of reported usability issues. Problem severity is not affected by any trait. Valence ratings are significantly affected by Conscientiousness, whereas Agreeableness, Emotional Stability and Openness significantly affect arousal ratings. Finally, Openness has a significant effect on the number of detected peaks in user's skin conductance

A Framework for Students Profile Detection

Some of the biggest problems tackling Higher Education Institutions are students’ drop-out and academic disengagement. Physical or psychological disabilities, social-economic or academic marginalization, and emotional and affective problems, are some of the factors that can lead to it. This problematic is worsened by the shortage of educational resources, that can bridge the communication gap between the faculty staff and the affective needs of these students. This dissertation focus in the development of a framework, capable of collecting analytic data, from an array of emotions, affects and behaviours, acquired either by human observations, like a teacher in a classroom or a psychologist, or by electronic sensors and automatic analysis software, such as eye tracking devices, emotion detection through facial expression recognition software, automatic gait and posture detection, and others. The framework establishes the guidance to compile the gathered data in an ontology, to enable the extraction of patterns outliers via machine learning, which assist the profiling of students in critical situations, like disengagement, attention deficit, drop-out, and other sociological issues. Consequently, it is possible to set real-time alerts when these profiles conditions are detected, so that appropriate experts could verify the situation and employ effective procedures. The goal is that, by providing insightful real-time cognitive data and facilitating the profiling of the students’ problems, a faster personalized response to help the student is enabled, allowing academic performance improvements

Computer detection of spatial visualization in a location-based task

An untapped area of productivity gains hinges on automatic detection of user cognitive characteristics. One such characteristic, spatial visualization ability, relates to users’ computer performance. In this dissertation, we describe a novel, behavior-based, spatial visualization detection technique. The technique does not depend on sensors or knowledge of the environment and can be adopted on generic computers. In a Census Bureau location-based address verification task, detection rates exceeded 80% and approached 90%

Roadside aesthetic appeal, driver behaviour and safety

Earlier publications report that on average the accident rate per hour of driver exposure is virtually the same across samples of different road sections. Both physiological and cognitive measures of the level of risk perceived, taken from drivers while passing different road sections, have shown substantial positive correlations with the police-recorded accident rate per km of the road sections in which the driving was done. Taken together, these studies indicate that where the historical accident rate is high, drivers on average maintain lower speeds, and vice versa. Apparently then, drivers are sensitive to environmental features that are associated with the accident rate per km driven, and they adjust their behaviour accordingly. This paper presents an analytical discussion of pertinent literature. Attention is drawn to the psychological effects of roadside scenery as documented in the literature. In contrast with the assumption that roadside trees distract travellers from the driving task and thus add to collision risk, several studies actually support the notion that the presence of trees along the roadside has a calming and restorative effect on the state of mind of passing drivers and leads them to lower moving speeds. Moreover, there is evidence that both driver (semi-permanent) traits and (temporary) states have an effect on their choice between more or less scenic roads. In order to overcome some of the limitations of the evidence gathered so far (simulation, quasi-experimentation in the field, and small sample sizes), this paper proposes that a time-based accident rate for different road sections be determined, and this with the ultimate aim to identify the distinctive geometric and scenic road features that make drivers run more risk per hour in certain road sections, and less in others than they do on average

Stress Reduction Using Bilateral Stimulation in Virtual Reality

The goal of this research is to integrate Virtual Reality (VR) with the bilateral stimulation used in EMDR as a tool to relieve stress. We created a 15 minutes relaxation training program for adults in a virtual, relaxing environment in form of a walk in the woods. The target platform for the tool is HTC Vive, however it can be easily ported to other VR platforms. An integral part of this tool is a set of sensors, which serves as physiological measures to evaluate the effectiveness of such system. What is more, the system integrate visual (passing sphere), auditory (surround sound) and tactile signals (vibration of controllers). A pilot treatment programme, incorporating the above mentioned VR system, was carried out. Experimental group consisting of 28 healthy adult volunteers (office workers), participated in three different sessions of relaxation training. Before starting, baseline features such as subjectively perceived stress, mood, heart rate, galvanic skin response and muscle response were registered. The monitoring of physiological indicators is continued during the training session and one minute after its completion. Before and after the session, volunteers were asked to re-fill questionnaires regarding the current stress level and mood. The obtained results were analyzed in terms of variability over time: before, during and after the session

Physiopad: development of a non-invasive game controller toolkit to study physiological responses for Game User Research

Os jogos afectivos usam as respostas fisiológicas do jogador para criar um ambiente adequado ao estado emocional do utilizador. A investigação destes jogos tem sido explorada nos últimos anos. Estas experiências, contudo, ainda requerem sistemas complexos e difíceis de utilizar. Nesta dissertação, é proposta a construção de um dispositivo capaz de ler dados fisiológicos de forma não invasiva e que seja de fácil utilização. Este aparelho faz a leitura do ritmo cardíaco e dos níveis de excitação do jogador, além disso foi criado um software para interligar com o dispositivo. Utilizando um comando da PlayStation 3 e um BITalino, o dispositivo é capaz de fazer a aquisição do sinal PPG e sinal EDA durante o jogo. O software analisa os sinais do comando, calcula o ritmo cardíaco e mede os níveis de excitação em tempo real. Foi realizada uma experiência utilizando biofeedback positivo e negativo, com o objectivo de testar a integração entre o software e o hardware. Não será no imediato que os dispositivos deste género sejam disponibilizados comercialmente. Os resultados são, no entanto, promissores. O cálculo do ritmo cardíaco em tempo real tem apenas uma diferença de 5 batimentos por minuto em relação ao ritmo cardíaco real do jogador. Apesar de os testes com o EDA serem inconclusivos, pode-se verificar que foi possível construir um sistema para ler os dados fisiológicos sendo mais económico do que os seus pares, sem comprometer a fiabilidade dos dados.Affective games are a genre of games that use the physiological responses from the player to adapt the gameplay to a more enjoyable emotional state and experience. Physiological responses and affective games have been studied vastly over the years. However, the setups used in these interventions are very intrusive and are complex to set up. In this project, it is purposed to build a non-invasive and easy-to-set-up toolkit that records physiological data. This toolkit records the player's heart rate and arousal levels and was decomposed into software and hardware. Using a PS3 game controller replica and a BITalino, a physiological game controller which can record heart rate and arousal during gameplay was built. The software interfaces with the gamepad, processes the physiological signals and sends this information to the game. An experiment with a positive biofeedback condition and negative biofeedback condition was conducted. This experiment showed that even though more work must be done until these type of devices could be commercially available, the results are promising. This toolkit’s heart rate values, when compared with other more traditional acquisition devices, were very similar, being on average only 5 BMP lower than the actual heart rate, proving that is possible to build more affordable non-invasive physiological hardware without compromising the signal's accuracy

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