Giorgos A. Giannakakis

Stress and anxiety detection using facial cues from videos

Abstract This study develops a framework for the detection and analysis of stress/anxiety emotional states through video-recorded facial cues. A thorough experimental protocol was established to induce systematic variability in affective states (neutral, relaxed and stressed/anxious) through a variety of external and internal stressors. The analysis was focused mainly on non-voluntary and semi-voluntary facial cues in order to estimate the emotion representation more objectively. Features under investigation included eye-related events, mouth activity, head motion parameters and heart rate estimated through camera-based photoplethysmography. A feature selection procedure was employed to select the most robust features followed by classification schemes discriminating between stress/anxiety and neutral states with reference to a relaxed state in each experimental phase. In addition, a ranking transformation was proposed utilizing self reports in order to investigate the correlation of facial parameters with a participant perceived amount of stress/anxiety. The results indicated that, specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety.

Methods for Seizure Detection and Prediction: An Overview

Epilepsy is one of the most common neurological diseases and the most common neurological chronic disease in childhood. Electroencephalography (EEG) still remains one of the most useful and effective tools in understanding and treatment of epilepsy. To this end, many computational methods have been developed for both the detection and prediction of epileptic seizures. Techniques derived from linear/nonlinear analysis, chaos, information theory, morphological analysis, model-based analysis, all present different advantages, disadvantages, and limitations. Recently, there is the notion of selecting and combining the most robust features from different methods for revealing various signals’ characteristics and making more reliable assumptions. Finally, intelligent classifiers are employed in order to distinguish epileptic state out of normal states. This chapter reviews the most widely adopted algorithms for the detection and prediction of epileptic seizures, emphasizing on information theory based and entropy indices. Each method’s accuracy has been evaluated through performance measures, assessing the ability of automatic seizure detection/ prediction.

Depression Assessment by Fusing High and Low Level Features from Audio, Video, and Text

Depression is a major cause of disability world-wide. The present paper reports on the results of our participation to the depression sub-challenge of the sixth Audio/Visual Emotion Challenge (AVEC 2016), which was designed to compare feature modalities (audio, visual, interview transcript-based) in gender-based and gender-independent modes using a variety of classification algorithms. In our approach, both high and low level features were assessed in each modality. Audio features were extracted from the low-level descriptors provided by the challenge organizers. Several visual features were extracted and assessed including dynamic characteristics of facial elements (using Landmark Motion History Histograms and Landmark Motion Magnitude), global head motion, and eye blinks. These features were combined with statistically derived features from pre-extracted features (emotions, action units, gaze, and pose). Both speech rate and word-level semantic content were also evaluated. Classification results are reported using four different classification schemes: i) gender-based models for each individual modality, ii) the feature fusion model, ii) the decision fusion model, and iv) the posterior probability classification model. Proposed approaches outperforming the reference classification accuracy include the one utilizing statistical descriptors of low-level audio features. This approach achieved f1-scores of 0.59 for identifying depressed and 0.87 for identifying not-depressed individuals on the development set and 0.52/0.81, respectively for the test set.

Comparison of blind source separation algorithms for optical heart rate monitoring

Monitoring of physiological signals of an individual via remote and contactless means is an important scientific challenge, whose resolution will enable the development of novel, non-intrusive mHealth and wellness-management systems and services. In this paper, the performance of three blind source separation algorithms for the optical estimation of the heart rate have been studied. The objective is to perform a comparative evaluation of their accuracy and convergence capability, for the optical estimation of the heart rate.

Absence seizure epilepsy detection using linear and nonlinear eeg analysis methods

In this study, we investigated three measures capable of detecting absence seizures with increased sensitivity based on different underlying assumptions. Namely, an information-based method known as Approximate Entropy, a nonlinear alternative (Order Index), and a linear variance analysis approach. The results on the long-term EEG data suggest increased accuracy in absence seizure detection achieving sensitivity as high as 97.33% with no further application of any sophisticated classification scheme.

Synchronization coupling investigation using ICA cluster analysis in resting MEG signals in reading difficulties

The understanding of the mechanisms of human brain is a demanding issue for neuroscience research. Physiological studies acknowledge the usefulness of synchronization coupling in the study of dysfunctions associated with reading difficulties. Magnetoencephalogram (MEG) is a useful tool towards this direction having been assessed for its superior accuracy over other modalities. In this paper we consider synchronization features for identifying brain operations. Independent Component Analysis (ICA) is applied on MEG surface signals in controls and children with reading difficulties and are clustered to representative components. Then, coupling measures of mutual information and partial directed coherence are estimated in order to reveal dysfunction of cerebral networks and its related coordination.

Mirror mirror on the wall… An intelligent multisensory mirror for well-being self-assessment

The face reveals the healthy status of an individual, through a combination of physical signs and facial expressions. The project SEMEOTICONS is translating the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, images, and 3D scans of the face. SEMEOTICONS is developing a multisensory platform, in the form of a smart mirror, looking for signs related to cardio-metabolic risk. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. Building the multisensory mirror requires addressing significant scientific and technological challenges, from touch-less data acquisition, to real-time processing and integration of multimodal data.

An approach to absence epileptic seizures detection using Approximate Entropy

Epilepsy is one of the most common chronic neurological diseases and the most common neurological chronic disease of childhood. The electroencephalogram (EEG) signal provides significant information neurologists take into consideration in the investigation and analysis of epileptic seizures. The Approximate Entropy (ApEn) is a formulated statistical parameter commonly used to quantify the regularity of a time series data of physiological signals. In this paper ApEn is used in order to detect the onset of epileptic seizures. The results show that the method provides promising results towards efficient detection of onset and ending of seizures, based on analyzing the corresponding EEG signals. ApEn parameters affect the methods behavior, suggesting that a more detailed study and a consistent methodology of their determination should be established. A preliminary analysis for the proper determination of these parameters is performed towards improving the results.

Extraction of facial features as indicators of stress and anxiety.

Stress and anxiety heavily affect the human wellbeing and health. Under chronic stress, the human body and mind suffers by constantly mobilizing all of its resources for defense. Such a stress response can also be caused by anxiety. Moreover, excessive worrying and high anxiety can lead to depression and even suicidal thoughts. The typical tools for assessing these psycho-somatic states are questionnaires, but due to their shortcomings, by being subjective and prone to bias, new more robust methods based on facial expression analysis have emerged. Going beyond the typical detection of 6 basic emotions, this study aims to elaborate a set of facial features for the detection of stress and/or anxiety. It employs multiple methods that target each facial region individually. The features are selected and the classification performance is measured based on a dataset consisting 23 subjects. The results showed that with feature sets of 9 and 10 features an overall accuracy of 73% is reached.

Mirror Mirror on the Wall... An Unobtrusive Intelligent Multisensory Mirror for Well-Being Status Self-Assessment and Visualization

A persons well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos, and three-dimensional scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data.