[STAGE MASTER] - Explainable Feature Selection for Stress and Emotion Monitoring Systems Using [...]

Cesi; Lyon; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A leading educational institution in Lyon is offering an internship focused on developing an explainable feature selection framework for stress and emotion monitoring systems. The candidate will work with the WESAD dataset, optimizing it for real-time applications while integrating explainable AI techniques to ensure interpretability. This role is ideal for students or recent graduates with a background in Computer Science and a passion for applied research in mental health technology.

Prestations

Hands-on research experience

Opportunity for publication

Qualifications

• Experience with machine learning models and data analysis.
• Proficiency in programming languages such as Python.
• Understanding of explainable AI frameworks.

Responsabilités

• Develop a feature selection framework for physiological data.
• Integrate explainable AI techniques to enhance model interpretability.
• Optimize the framework for real-time stress monitoring applications.

Connaissances

Data preprocessing

Feature extraction

Machine learning

Explainable AI techniques

Statistical analysis

Formation

Relevant degree in Computer Science or related field

Outils

Python

SHAP

LIME

Description du poste

Internship Topic

Stress and emotion monitoring systems play a crucial role in mental health management, especially given the global rise in psychological disorders. Wearable devices equipped with sensors such as ECG (electrocardiogram), PPG (photoplethysmogram), EDA (electrodermal activity), and motion sensors provide a rich source of data for stress and emotion analysis. However, the high dimensionality and noise present in these multimodal datasets pose challenges for efficient processing and interpretability. Feature selection is essential for building robust and interpretable models. Recent advancements in explainable AI (XAI) emphasize the need to ensure that selected features provide understandable information about emotional and stress states, which strengthens trust and adoption by healthcare professionals. The WESAD (Wearable Stress and Affect Detection) dataset offers an ideal platform to develop and test these approaches. It includes multimodal physiological and motion data collected during controlled experiments, labeled for different stress and emotional states.

Internship Objective

Develop an explainable feature selection framework tailored to multimodal physiological data, enabling precise and interpretable monitoring of stress and emotions using the WESAD dataset.

Research Objectives

Feature Selection Framework:

• Design a feature selection method to identify the most relevant features in physiological signals (ECG, EDA) and motion data (accelerometer).
• Ensure robustness against noise and variability in physiological data.

Explainability and Interpretability:

• Integrate explainable AI techniques (e.g., SHAP, LIME) to explain the importance of selected features in detecting stress and emotional states.
• Ensure interpretability aligns with clinical relevance (e.g., heart rate variability for stress detection).

Real-Time Application:

• Optimize the framework for low-latency applications suitable for real-time stress monitoring.

Methodology

1. Data Preprocessing: Extract and preprocess signals from the WESAD dataset (ECG, EDA, PPG, accelerometer). Synchronize modalities and address issues related to noise and missing data.

2. Feature Extraction and Selection: Extract domain-specific features (e.g., heart rate variability, signal entropy, motion patterns). Develop a hybrid feature selection framework:

   • Filters: Use statistical measures like correlation and mutual information.
   • Wrappers: Implement recursive or sequential feature selection.
   • Embedded Methods: Leverage tree-based models (e.g., Random Forest) to analyze feature importance.

3. Explainable AI Integration: Use SHAP or LIME to evaluate and visualize the contribution of selected features to model predictions. Ensure interpretability aligns with clinical relevance (e.g., heart rate variability for stress detection).

4. Model Development and Validation: Train machine learning models (e.g., SVM, LSTM, or CNN) using selected features. Evaluate performance using metrics such as accuracy, precision, F1-score, real-time efficiency (latency, resource consumption), and explainability (variable importance analysis). Compare results with state-of-the-art methods in stress monitoring.

Expected Outcomes

1. A robust and explainable framework for feature selection in stress and emotion monitoring.
2. Improved accuracy and efficiency in stress detection models.
3. Interpretable insights into physiological and motion indicators of stress, contributing to mental health research.
4. Publication in a journal or conference focused on AI or health.

Introduction to the Laboratory

CESI LINEACT (Digital Innovation Laboratory for Companies and Learnings at the service of the territories competitiveness) is the CESI group laboratory whose activities are implemented on CESI campuses. CESI LINEACT (EA 7527) is a Digital Innovation Laboratory for Business and Learning at the service of the Competitiveness of Territories, focusing on applied research close to companies and in partnership with them. Its human-centered approach with a territorial network and links with training centers places the user at the center of its problems and approaches technology with a practical orientation.

Research Themes

Two interdisciplinary scientific themes guide the research:

• Theme 1 — Learning and Innovation: Focuses on cognitive sciences, social sciences, management sciences, training sciences, and innovation sciences to understand how environments and instrumented situations affect learning, creativity, and innovation.
• Theme 2 — Engineering and Digital Tools: Focuses on digital sciences and engineering, modeling, simulation, optimization, data analysis, decision support tools, and digital twins with virtual or augmented environments.

Research intersects with the Factory of the Future and the City of the Future.

References

1. chmidt, P., Reiss, A., Duerichen, R., Marberger, C., & Van Laerhoven, K. (2018). WESAD: A Multimodal Dataset for Wearable Stress and Affect Detection. Proceedings of the 20th ACM International Conference on Multimodal Interaction.
2. Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems.
3. Healey, J., & Picard, R. W. (2005). Detecting Stress During Real-World Driving Tasks Using Physiological Sensors. IEEE Transactions on Intelligent Transportation Systems.