Logging Stress and Anxiety Using a Gamified Mobile-based EMA Application, and Emotion Recognition Using a Personalized Machine Learning Approach

According to American Psychological Association (APA) more than 9 in 10 (94 percent) adults believe that stress can contribute to the development of major health problems, such as heart disease, depression, and obesity. Due to the subjective nature of stress, and anxiety, it has been demanding to measure these psychological issues accurately by only relying on objective means. In recent years, researchers have increasingly utilized computer vision techniques and machine learning algorithms to develop scalable and accessible solutions for remote mental health monitoring via web and mobile applications. To further enhance accuracy in the field of digital health and precision diagnostics, there is a need for personalized machine-learning approaches that focus on recognizing mental states based on individual characteristics, rather than relying solely on general-purpose solutions. This thesis focuses on conducting experiments aimed at recognizing and assessing levels of stress and anxiety in participants. In the initial phase of the study, a mobile application with broad applicability (compatible with both Android and iPhone platforms) is introduced (we called it STAND). This application serves the purpose of Ecological Momentary Assessment (EMA). Participants receive daily notifications through this smartphone-based app, which redirects them to a screen consisting of three components. These components include a question that prompts participants to indicate their current levels of stress and anxiety, a rating scale ranging from 1 to 10 for quantifying their response, and the ability to capture a selfie. The responses to the stress and anxiety questions, along with the corresponding selfie photographs, are then analyzed on an individual basis. This analysis focuses on exploring the relationships between self-reported stress and anxiety levels and potential facial expressions indicative of stress and anxiety, eye features such as pupil size variation and eye closure, and specific action units (AUs) observed in the frames over time. In addition to its primary functions, the mobile app also gathers sensor data, including accelerometer and gyroscope readings, on a daily basis. This data holds potential for further analysis related to stress and anxiety. Furthermore, apart from capturing selfie photographs, participants have the option to upload video recordings of themselves while engaging in two neuropsychological games. These recorded videos are then subjected to analysis in order to extract pertinent features that can be utilized for binary classification of stress and anxiety (i.e., stress and anxiety recognition). The participants that will be selected for this phase are students aged between 18 and 38, who have received recent clinical diagnoses indicating specific stress and anxiety levels. In order to enhance user engagement in the intervention, gamified elements - an emerging trend to influence user behavior and lifestyle - has been utilized. Incorporating gamified elements into non-game contexts (e.g., health-related) has gained overwhelming popularity during the last few years which has made the interventions more delightful, engaging, and motivating. In the subsequent phase of this research, we conducted an AI experiment employing a personalized machine learning approach to perform emotion recognition on an established dataset called Emognition. This experiment served as a simulation of the future analysis that will be conducted as part of a more comprehensive study focusing on stress and anxiety recognition. The outcomes of the emotion recognition experiment in this study highlight the effectiveness of personalized machine learning techniques and bear significance for the development of future diagnostic endeavors. For training purposes, we selected three models, namely KNN, Random Forest, and MLP. The preliminary performance accuracy results for the experiment were 93%, 95%, and 87% respectively for these models

Collaborative Learning in Computer Vision

The science of designing machines to extract meaningful information from digital images, videos, and other visual inputs is known as Computer Vision (CV). Deep learning algorithms cope CV problems by automatically learning task-specific features. Especially, Deep Neural Networks (DNNs) have become an essential component in CV solutions due to their ability to encode large amounts of data and capacity to manipulate billions of model parameters. Unlike machines, humans learn by rapidly constructing abstract models. This is undoubtedly due to the fact that good teachers supply their students with much more than just the correct answer; they also provide intuitive comments, comparisons, and explanations. In deep learning, the availability of such auxiliary information at training time (but not at test time) is referred to as learning by Privileged Information (PI). Typically, predictions (e.g., soft labels) produced by a bigger and better network teacher are used as structured knowledge to supervise the training of a smaller network student, helping the student network to generalize better than that trained from scratch. This dissertation focuses on the category of deep learning systems known as Collaborative Learning, where one DNN model helps other models or several models help each other during training to achieve strong generalization and thus high performance. The question we address here is thus the following: how can we take advantage of PI for training a deep learning model, knowing that, at test time, such PI might be missing? In this context, we introduce new methods to tackle several challenging real-world computer vision problems. First, we propose a method for model compression that leverages PI in a teacher-student framework along with customizable block-wise optimization for learning a target-specific lightweight structure of the neural network. In particular, the proposed resource-aware optimization is employed on suitable parts of the student network while respecting the expected resource budget (e.g., floating-point operations per inference and model parameters). In addition, soft predictions produced by the teacher network are leveraged as a source of PI, forcing the student to preserve baseline performance during network structure optimization. Second, we propose a multiple-model learning method for action recognition, specifically devised for challenging video footages in which actions are not explicitly visualized, but rather, only implicitly referred. We use such videos as stimuli and involve a large sample of subjects to collect a high-definition EEG and video dataset. Next, we employ collaborative learning in a multi-modal setting i.e., the EEG (teacher) model helps the video (student) model by distilling the knowledge (implicit meaning of visual stimuli) to it, sharply boosting the recognition performance. The goal of Unsupervised Domain Adaptation (UDA) methods is to use the labeled source together with the unlabeled target domain data to train a model that generalizes well on the target domain. In contrast, we cast UDA as a pseudo-label refinery problem in the challenging source-free scenario i.e., in cases where the source domain data is inaccessible during training. We propose Negative Ensemble Learning (NEL) technique, a unified method for adaptive noise filtering and progressive pseudo-label refinement. In particular, the ensemble members collaboratively learn with a Disjoint Set of Residual Labels, an outcome of the output prediction consensus, to refine the challenging noise associated with the inferred pseudo-labels. A single model trained with the refined pseudo-labels leads to superior performance on the target domain, without using source data samples at all. We conclude this dissertation with a method extending our previous study by incorporating Continual Learning in the Source-Free UDA. Our new method comprises of two stages: a Source-Free UDA pipeline based on pseudo-label refinement, and a procedure for extracting class-conditioned source-style images by leveraging the pre-trained source model. While stage 1 holds the same collaborative peculiarities, in stage 2, the collaboration exists in an indirect manner i.e., it is the source model that provides the only possibility to generate source-style synthetic images which eventually helps the final model in preserving good performance on both source and target domains. In each study, we consider heterogeneous CV tasks. Nevertheless, with an extensive pool of experiments on various benchmarks carrying diverse complexities and challenges, we show that the collaborative learning framework outperforms the related state-of-the-art methods by a considerable margin

A Novel Loss Function Utilizing Wasserstein Distance to Reduce Subject-Dependent Noise for Generalizable Models in Affective Computing

Emotions are an essential part of human behavior that can impact thinking, decision-making, and communication skills. Thus, the ability to accurately monitor and identify emotions can be useful in many human-centered applications such as behavioral training, tracking emotional well-being, and development of human-computer interfaces. The correlation between patterns in physiological data and affective states has allowed for the utilization of deep learning techniques which can accurately detect the affective states of a person. However, the generalisability of existing models is often limited by the subject-dependent noise in the physiological data due to variations in a subject's reactions to stimuli. Hence, we propose a novel cost function that employs Optimal Transport Theory, specifically Wasserstein Distance, to scale the importance of subject-dependent data such that higher importance is assigned to patterns in data that are common across all participants while decreasing the importance of patterns that result from subject-dependent noise. The performance of the proposed cost function is demonstrated through an autoencoder with a multi-class classifier attached to the latent space and trained simultaneously to detect different affective states. An autoencoder with a state-of-the-art loss function i.e., Mean Squared Error, is used as a baseline for comparison with our model across four different commonly used datasets. Centroid and minimum distance between different classes are used as a metrics to indicate the separation between different classes in the latent space. An average increase of 14.75% and 17.75% (from benchmark to proposed loss function) was found for minimum and centroid euclidean distance respectively over all datasets.Comment: 9 page

CNN and LSTM-Based Emotion Charting Using Physiological Signals

Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence—High Arousal, High Valence—Low Arousal, Low Valence—High Arousal, and Low Valence—Low Arousal. Emotion elicitation average accuracy of 98.73% is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral-and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.Peer reviewe

A dataset of continuous affect annotations and physiological signals for emotion analysis

From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, direct, real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were made of ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented.Comment: Dataset available at: https://rmc.dlr.de/download/CASE_dataset/CASE_dataset.zi

Toward the application of XAI methods in EEG-based systems

An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to poor generalisation performance in BCI classification systems used in different sessions, also from the same subject. In this paper, we start from the hypothesis that the Dataset Shift problem can be alleviated by exploiting suitable eXplainable Artificial Intelligence (XAI) methods to locate and transform the relevant characteristics of the input for the goal of classification. In particular, we focus on an experimental analysis of explanations produced by several XAI methods on an ML system trained on a typical EEG dataset for emotion recognition. Results show that many relevant components found by XAI methods are shared across the sessions and can be used to build a system able to generalise better. However, relevant components of the input signal also appear to be highly dependent on the input itself.Comment: Accepted to be presented at XAI.it 2022 - Italian Workshop on Explainable Artificial Intelligenc

Approaches, applications, and challenges in physiological emotion recognition — a tutorial overview

An automatic emotion recognition system can serve as a fundamental framework for various applications in daily life from monitoring emotional well-being to improving the quality of life through better emotion regulation. Understanding the process of emotion manifestation becomes crucial for building emotion recognition systems. An emotional experience results in changes not only in interpersonal behavior but also in physiological responses. Physiological signals are one of the most reliable means for recognizing emotions since individuals cannot consciously manipulate them for a long duration. These signals can be captured by medical-grade wearable devices, as well as commercial smart watches and smart bands. With the shift in research direction from laboratory to unrestricted daily life, commercial devices have been employed ubiquitously. However, this shift has introduced several challenges, such as low data quality, dependency on subjective self-reports, unlimited movement-related changes, and artifacts in physiological signals. This tutorial provides an overview of practical aspects of emotion recognition, such as experiment design, properties of different physiological modalities, existing datasets, suitable machine learning algorithms for physiological data, and several applications. It aims to provide the necessary psychological and physiological backgrounds through various emotion theories and the physiological manifestation of emotions, thereby laying a foundation for emotion recognition. Finally, the tutorial discusses open research directions and possible solutions