International audienceStress is a complex phenomenon that impacts the body and the mind at several levels. It has been studied for more than a century from different perspectives, which result in different definitions and different ways to assess the presence of stress. This paper introduces a methodology for analyzing multimodal stress detection results by taking into account the variety of stress assessments. As a first step, we have collected video, depth and physiological data from 25 subjects in a stressful situation: a socially evaluated mental arithmetic test. As a second step, we have acquired 3 different assessments of stress: self-assessment, assessments from external observers and assessment from a physiology expert. Finally, we extract 101 behavioural and physiological features and evaluate their predictive power for the 3 collected assessments using a classification task. Using multimodal features, we obtain average F1 scores up to 0.85. By investigating the composition of the best selected feature subsets and the individual feature classification performances, we show that several features provide valuable information for the classification of the 3 assessments: features related to body movement, blood volume pulse and heart rate. From a methodological point of view, we argue that a multiple assessment approach provide more robust results

The increasing usage of complex Machine Learning models for decision-making has raised interest in explainable artificial intelligence (XAI). In this work, we focus on the effects of providing accessible and useful explanations to non-expert users. More specifically, we propose generic XAI design principles for contextualizing and allowing the exploration of explanations based on local feature importance. To evaluate the effectiveness of these principles for improving users' objective understanding and satisfaction, we conduct a controlled user study with 80 participants using 4 different versions of our XAI system, in the context of an insurance scenario. Our results show that the contextualization principles we propose significantly improve user's satisfaction and is close to have a significant impact on user's objective understanding. They also show that the exploration principles we propose improve user's satisfaction. On the other hand, the interaction of these principles does not appear to bring improvement on both dimensions of users' understanding. CCS CONCEPTS• Human-centered computing → User studies; HCI theory, concepts and models.

International audienceThis paper introduces behavioural features for automatic stress detection, and a person-specific normalization to enhance the performance of our system. The presented features are all visual cues automatically extracted using video processing and depth data. In order to collect the necessary data, we conducted a lab study for stress elicitation using a time constrained arithmetic mental test. Then, we propose a set of body language features for stress detection. Experimental results using a SVM show that our model can detect stress with high accuracy (77%). Moreover, person specific normalization significantly improves classification results (from 67% to 77%). Also, the performance of each of the presented features is discussed

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