Machine Learning and Deep Learning for Negative Emotion, Stress and Pain Recognition

Dear Colleagues,

The aim of this interdisciplinary Special Issue is to discuss methods of machine learning, deep learning and data analytics for the classification of multimodal data processing in e-health applications, and particularly the detection and classification of pain, stress and negative emotions.

Nowadays, AI technologies based on methods developed in signal processing, data analytics and machine learning—in particular learning in deep artificial neural networks from multimodal data, including videos of facial expressions, poses and gestures, audio data or bio-physiological data (such as eye tracking, respiration, skin conductance, ECG, EEG, FMRT, EMG and others)—are applied for such complex pattern recognition tasks.

For instance, in a hospital setting, the automatic classification of pain events is crucial for the development of efficient and objective rules for pain assessment. In recent years, various machine learning algorithms and, increasingly, deep learning algorithms have been used for multimodal pain recognition. Relevant databases were collected in laboratory environments where pain was artificially generated.

More recently, natural data have also been collected from intensive care unit patients after surgery.

Research papers relating to this interdisciplinary field of research are welcome, including the following topics (but not limited to them):

• Facial expression analysis;
• Pain recognition using paralinguistic signals;
• Biophysiological modalities in pain recognition;
• Neural patterns for pain in EEG and FMRT;
• Multimodal signal processing in pain recognition;
• Sensor systems for pain classification;
• Data collection for pain recognition;
• Annotation of pain data;
• Information fusion;
• Machine learning;
• Feature design and feature evaluation;
• Artificial neural networks and deep learning;
• Active learning;
• Semi-supervised learning;
• Transfer learning;
• Learning of personalized models;
• Evaluation of pain recognition systems;
• Human-computer interaction for healthcare and well-being;
• Clinical studies.

Prof. Dr. Friedhelm Schwenker
Guest Editor

Manuscript Submission Information

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• facial expression analysis
• pain recognition using paralinguistic signals
• biophysiological modalities in pain recognition
• neural patterns for pain in in EEG and FMRT
• multimodal signal processing in pain recognition
• sensor systems for pain classification
• data collection for pain recognition
• annotation of pain data
• information fusion
• machine learning
• feature design and feature evaluation
• artificial neural networks and deep learning
• active learning
• semi-supervised learning
• transfer learning
• learning of personalized models
• evaluation of pain recognition systems
• human computer interaction for healthcare and well-being
• clinical studies