Silent UX? Erfassung der User Experience von intelligenten Heimanwendungen

Vernetzte Geräte dringen immer weiter in unseren Alltag vor. Die mit ihnen propagierte örtliche Entkoppelung des Nutzers vom Produktstandort stellt bestehende Usability- und User-Experience-Methoden vor Probleme. Daher wird hier untersucht, ob psychophysiologische Methoden die Erfassung des Interaktionserlebnisses erlauben. Am Beispiel eines intelligenten Lautsprechers wird getestet, ob das Stresserleben, der kognitive Workload sowie die Emotionen während der Produktinteraktion gleichzeitig mittels psychophysiologischen Maßen erfasst werden können. Diese Studie erweitert bestehende Befunde zum Einsatz von psychophysiologischen Maßen zur Erfassung des Nutzungserlebens und bietet eine gute Grundlage zur Echtzeiterfassung des Nutzererlebens

Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

An Evaluation of Mouse and Keyboard Interaction Indicators towards Non-intrusive and Low Cost Affective Modeling in an Educational Context

AbstractIn this paper we propose a series of indicators, which derive from user's interactions with mouse and keyboard. The goal is to evaluate their use in identifying affective states and behavior changes in an e-learning platform by means of non-intrusive and low cost methods. The approach we have followed study user's interactions regardless of the task being performed and its presentation, aiming at finding a solution applicable in any domain. In particular, mouse movements and clicks, as well as keystrokes were recorded during a math problem solving activity where users involved in the experiment had not only to score their degree of valence (i.e., pleasure versus displeasure) and arousal (i.e., high activation versus low activation) of their affective states after each problem by using the Self-Assessment-Manikin scale, but also type a description of their own feelings. By using that affective labeling, we evaluated the information provided by these different indicators processed from the original user's interactions logs. In total, we computed 42 keyboard indicators and 96 mouse indicators

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use

Assessment of students’ cognitive–affective states in learning within a computer-based environment: Effects on performance

Students’ cognitive-affective states are human elements that are crucial in the design of computer-based learning (CBL) systems.This paper presents an investigation of students’ cognitiveaffective states (i.e., engaged concentration, anxiety, and boredom) when they learn a particular course within CBL systems.The results of past studies by other researchers suggested that certain cognitive-affective states; particularly boredom and anxiety could negatively influence learning in a computer-based environment.This paper investigates the types of cognitive-affective state that students experience when they learn through a specifi c instance of CBL (i.e., a content sequencing system). Further, research was carried to understand whether the cognitive-affective states would infl uence students’ performance within the environment.A one-way between-subject-design experiment was conducted utilizing four instruments (i) CBL systems known as IT-Tutor for learning computer network, (ii) a pre-test, (iii) a post-test, and (iv) self-report inventory to capture the students’ cognitive-affective states. A cluster analysis and discriminant function analysis were employed to identify and classify the students’ cognitiveaffective states.Students were classifi ed according to their prior knowledge to element the effects of it on performance.Then,non-parametric statistical tests were conducted on different pairs of cluster of the cognitive-affective states and prior knowledge to determine differences on students’ performance. The results of this study suggested that all the three cognitive-affective states were experienced by the students. The cognitive-affective states were found to have positive effects on the students’ performance.This study revealed that disengaged cognitive-affective states, particularly boredom can improve learning performance for lowprior knowledge students

Mise en œuvre d’une méthode de Data Mining pour appréhender le comportement d’un sujet en état de tunnélisation attentionnelle

Dans l’aéronautique, on considère que 80% des accidents sont dus à une erreur humaine dans l’aviation civil et militaire (O'Hare, Wiggins, Batt, & Morrison, 1994) (Wiegmann & Shappell, 2003). Ces statistiques ont donc amené nombre de scientifiques à s’intéresser au sujet des facteurs humains. L’idée est d’améliorer la sécurité aérienne en comprenant mieux le comportement humain. On s’aperçoit en effet que certains accidents rejoués en simulateur par d’autres pilotes expérimentés conduisent parfois au même crash (Wanner & Wanner, 1999). C’est parfois l’environnement qui conduit à l’erreur humaine. Ainsi il est intéressant de rechercher des moyens d’aider l’opérateur dans sa tâche. Ce n’est pas chose si aisée. Van Eslande et al (Van Eslande, Erreur de conduite et besoin d’aide : une approche accidentologique, 2001) (Van Eslande, Alberton, Nachtergaële, & Blancher, 1997) postulent que le comportement des automobilistes est essentiellement conditionné par les infrastructures routières. Il a été remarqué que les conflits étaient un précurseur remarquable d’erreurs humaines conduisant à l’accident. Des confits entre l’humain et la machine, ou entre l’opérateur et la tour de contrôle, ou encore entre le pilote et le co-pilote. L’étude des conflits s’avère alors un thème pertinent pour les facteurs humains. C’est dans ce domaine que nous travaillons au CAS au sein de l’ISAE. Le rapport sera constitué de trois grandes sections. Dans un premier temps nous présenterons l’environnement de travail à l’ISAE. Puis dans les parties suivantes nous définirons de manière plus précise ce qu’est la « tunnélisation attentionnelle » et décrirons plus en profondeur l’expérience du robot qui est notre base de travail. Enfin nous présenterons les résultats en termes de diagnostic de l’état d’un opérateur

A prototype for a conversational companion for reminiscing about images

This work was funded by the COMPANIONS project sponsored by the European Commission as part of the Information Society Technologies (IST) programme under EC grant number IST-FP6-034434. Companions demonstrators can be seen at: http://www.dcs.shef.ac.uk/∼roberta/companions/Web/.This paper describes an initial prototype of the Companions project (www.companions-project.org): the Senior Companion (SC), designed to be a platform to display novel approaches to: (1) The use of Information Extraction (IE) techniques to extract the content of incoming dialogue utterances after an ASR phase. (2) The conversion of the input to RDF form to allow the generation of new facts from existing ones, under the control of a Dialogue Manager (DM), that also has access to stored knowledge and knowledge accessed in real time from the web, all in RDF form. (3) A DM expressed as a stack and network virtual machine that models mixed initiative in dialogue control. (4) A tuned dialogue act detector based on corpus evidence. The prototype platform was evaluated, and we describe this; it is also designed to support more extensive forms of emotion detection carried by both speech and lexical content, as well as extended forms of machine learning. We describe preliminary studies and results for these, in particular a novel approach to enabling reinforcement learning for open dialogue systems through the detection of emotion in the speech signal and its deployment as a form of a learned DM, at a higher level than the DM virtual machine and able to direct the SC’s responses to a more emotionally appropriate part of its repertoire. © 2010 Elsevier Ltd. All rights reserved.peer-reviewe

Under pressure: sensing stress of computer users

ABSTRACT Recognizing when computer users are stressed can help reduce their frustration and prevent a large variety of negative health conditions associated with chronic stress. However, measuring stress non-invasively and continuously at work remains an open challenge. This work explores the possibility of using a pressure-sensitive keyboard and a capacitive mouse to discriminate between stressful and relaxed conditions in a laboratory study. During a 30-minute session, 24 participants performed several computerized tasks consisting of expressive writing, text transcription, and mouse clicking. During the stressful conditions, the large majority of the participants showed significantly increased typing pressure (>79% of the participants) and more contact with the surface of the mouse (75% of the participants). We discuss the potential implications of this work and provide recommendations for future work