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

An information assistant system for the prevention of tunnel vision in crisis management

In the crisis management environment, tunnel vision is a set of bias in decision makers’ cognitive process which often leads to incorrect understanding of the real crisis situation, biased perception of information, and improper decisions. The tunnel vision phenomenon is a consequence of both the challenges in the task and the natural limitation in a human being’s cognitive process. An information assistant system is proposed with the purpose of preventing tunnel vision. The system serves as a platform for monitoring the on-going crisis event. All information goes through the system before arrives at the user. The system enhances the data quality, reduces the data quantity and presents the crisis information in a manner that prevents or repairs the user’s cognitive overload. While working with such a system, the users (crisis managers) are expected to be more likely to stay aware of the actual situation, stay open minded to possibilities, and make proper decisions

Comparing two haptic interfaces for multimodal graph rendering

This paper describes the evaluation of two multimodal interfaces designed to provide visually impaired people with access to various types of graphs. The interfaces consist of audio and haptics which is rendered on commercially available force feedback devices. This study compares the usability of two force feedback devices: the SensAble PHANToM and the Logitech WingMan force feedback mouse in representing graphical data. The type of graph used in the experiment is the bar chart under two experimental conditions: single mode and multimodal. The results show that PHANToM provides better performance in the haptic only condition. However, no significant difference has been found between the two devices in the multimodal condition. This has confirmed the advantages of using multimodal approach in our research and that low-cost haptic devices can be successful. This paper introduces our evaluation approach and discusses the findings of the experiment

Using Pupil Diameter to Measure Cognitive Load

In this paper, we will present a method for measuring cognitive load and online real-time feedback using the Tobii Pro 2 eye-tracking glasses. The system is envisaged to be capable of estimating high cognitive load states and situations, and adjust human-machine interfaces to the user's needs. The system is using well-known metrics such as average pupillary size over time. Our system can provide cognitive load feedback at 17-18 Hz. We will elaborate on our results of a HRI study using this tool to show it's functionality.Comment: Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606

Investigating the generalizability of EEG-based Cognitive Load Estimation Across Visualizations

We examine if EEG-based cognitive load (CL) estimation is generalizable across the character, spatial pattern, bar graph and pie chart-based visualizations for the nback~task. CL is estimated via two recent approaches: (a) Deep convolutional neural network, and (b) Proximal support vector machines. Experiments reveal that CL estimation suffers across visualizations motivating the need for effective machine learning techniques to benchmark visual interface usability for a given analytic task