Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

Dear Colleagues,

A myriad of modern intelligent sociotechnical systems makes use of human emotion and stress data. Different technologies are used to collect that data, like physiological sensors (e.g., EEG, ECG, electrodermal activity and skin conductance) and other non-intrusive sensors (e.g., piezo-vibration sensors, facial images, chairborne differential vibration sensors, bed-borne differential vibration sensors). Examples of such systems range from driver assistance systems, medical patient monitoring systems, and emotion-aware intelligent systems, up to complex collaborative robotics systems.

Emotion and stress classification from physiological signals is extremely challenging from various perspectives: (a) sensor-data quality and reliability; (b) classification performance (accuracy, precision, specificity, recall, F-measure); (c) robustness of subject-independent recognition; (d) portability of the classification systems to different environments; and (e) the estimation of the emotional state from a system-dynamical perspective.

This Special Issue invites contributions that address (i) sensing technologies and issues and (ii) machine learning techniques of relevance to tackle the challenges above. In particular, submitted papers should clearly show novel contributions and innovative applications covering, but not limited to, any of the following topics around emotion and stress recognition:

• Intrusive sensors systems and devices for capturing biosignals:
  • EEG sensor systems
  • ECG sensor systems
  • Electrodermal activity sensor systems
• Sensor data quality assessment and management
• Data pre-processing, noise filtering, and calibration concepts for biosignals
• Non-intrusive sensors technologies:
  • Visual sensors
  • Acoustic sensors
  • Vibration sensors
  • Piezo-electric sensors
• Emotion recognition using mobile phones and smart watches
• Body area sensor networks for emotion and stress studies
• Experimental datasets:
  • Datasets generation principles and concepts
  • Quality insurance
  • Emotion elicitation material and concepts
• Machine learning techniques for robust emotion recognition:
  • Graphical models
  • Neural network methods (LSTM networks, cellular neural networks);
  • Deep learning methods
  • Statistical learning
  • Multivariate empirical mode decomposition
  • Etc.
• Subject-independent emotion and stress recognition concepts and systems:
  • Facial expression-based systems
  • Speech-based systems
  • EEG-based systems
  • ECG-based systems
  • Electrodermal activity-based systems
  • Multimodal recognition systems
  • Sensor fusion concepts
  • Etc.
• Emotion and stress estimation-and-forecasting from a nonlinear dynamical system’s perspective:
  • Recursive quantitative analysis
  • Poincaré maps, fractal dimension analysis, Lyapunov exponents and entropies (e.g.: multiscale, permutation) of biosignals: EEG, ECG, speech, etc.
  • Regularized learning with nonlinear dynamical features of EEG, ECG, and speech signals
  • Complexity measurement and analysis of biosignals used for emotion recognition
  • Nonlinear features variability analysis
  • Dynamical graph convolutional neural networks
  • Etc.

Prof. Dr. Kyandoghere Kyamakya
Dr. Fadi Al-Machot
Dr. Ahmad Haj Mosa
Prof. Hamid Bouchachia
Dr. Jean Chamberlain Chedjou
Prof. Dr. Antoine Bagula
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.