Supervision : Séverine DUBUISSON

Co-supervision : DETYNIECKI Marcin

Multimodal detection of stress: evaluation of the impact of several assessment strategies

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.

2015-2022 Publications

• 2022
  • C. Bove, J. Aigrain, M.‑J. Lesot, Ch. Tijus, M. Detyniecki : “Contextualization and Exploration of Local Feature Importance Explanations to Improve Understanding and Satisfaction of Non-Expert Users”, IUI '22: 27^th International Conference on Intelligent User Interfaces, Helsinki, Finland, pp. 807-819, (ACM) (2022)
• 2021
  • C. Bove, J. Aigrain, M.‑J. Lesot, Ch. Tijus, M. Detyniecki : “Contextualising local explanations for non-expert users: an XAI pricing interface for insurance”, Joint Proceedings of the ACM IUI 2021 Workshops co-located with 26^th ACM Conference on Intelligent User Interfaces (ACM IUI 2021), vol. 2903, CEUR Workshop Proceedings, College Station, United States, (CEUR-WS.org) (2021)
• 2019
  • V. Ballet, †. Xavier, J. Aigrain, Th. Laugel, P. Frossard, M. Detyniecki : “Imperceptible Adversarial Attacks on Tabular Data”, NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness and Privacy (Robust AI in FS 2019), Vancouver, Canada (2019)
• 2016
  • J. Aigrain : “Détection de stress dans la gestuelle à partir de vidéos”, thesis, phd defence 12/05/2016, supervision Dubuisson, Séverine, co-supervision : Detyniecki, Marcin (2016)
  • J. Aigrain, A. Dapogny, K. Bailly, S. Dubuisson, M. Detyniecki, M. Chetouani : “On leveraging crowdsourced data for automatic perceived stress detection”, ICMI 2016 - 18^th ACM International Conference on Multimodal Interaction, Tokyo, Japan, pp. 113-120, (ACM Press) (2016)
  • J. Aigrain, M. Spodenkiewicz, S. Dubuisson, M. Detyniecki, D. Cohen, M. Chetouani : “Multimodal stress detection from multiple assessments”, IEEE Transactions on Affective Computing, vol. PP (99), pp. 1-1, (Institute of Electrical and Electronics Engineers) (2016)
• 2015
  • J. Aigrain, S. Dubuisson, M. Detyniecki, M. Chetouani : “Person-specific behavioural features for automatic stress detection”, 2015 11^th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia (2015)