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Multimodal detection of stress : evaluation of the impact of several assessment strategies. (Détection multimodale du stress : évaluation de l'impact de plusieurs stratégies de mesure)

It is now widely accepted that stress plays an important role in modern societies. It impacts the body and the mind at several levels and the association between stress and disease has been observed in several studies. However, there is no consensual definition of stress yet, and therefore there is no consensual way of assessing it either. Thus, although the quality of assessment is a key factor to build robust stress detection solutions, researchers have to choose among a wide variety of assessment strategies. This heterogeneity impacts the validity of comparing solutions among them. In this thesis, we evaluate the impact of several assessment strategies for stress detection. We first review how different fields of research define and assess stress. Then, we describe how we collected stress data along with multiple assessments. We also study the association between these assessments. We present the behavioural and physiological features that we extracted for our experiments. Finally, we present the results we obtained regarding the impact of assessment strategies on 1) data normalization, 2) feature classification performance and 3) on the design of machine learning algorithms. Overall, we argue that one has to take a global and comprehensive approach to design stress detection solutions.