AI-Assisted Emotion Recognition: Impacts on Mental Health Education and Learning Motivation

With the rapid advancements in artificial intelligence (AI) technology, its deployment in the field of education has gained considerable attention, particularly in the context of mental health education. Addressing the mounting academic and social pressures faced by contemporary students necessitates the utilization of cutting-edge techniques to accurately discern their emotional states and deliver customized learning resources. Existing methodologies for mental health education often fall short due to an over-reliance on educators’ experience and observations, as well as challenges in handling complex multimodal data. This research aims to investigate the integration of multimodal audio-visual features using a transformer architecture for emotion recognition. An enhanced probabilistic matrix factorization (PMF) model has been concurrently developed to facilitate tailored content recommendations for students. The goal is to provide a more accurate and effective approach to health education

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

Intelligent depression detection with asynchronous federated optimization.

The growth of population and the various intensive life pressures everyday deepen the competitions among people. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. The development of social networks such as Facebook, Twitter, Weibo, and QQ provides more convenient communication and provides a new emotional vent window. People communicate with their friends, sharing their opinions, and shooting videos to reflect their feelings. It provides an opportunity to detect depression in social networks. Although depression detection using social networks has reflected the established connectivity across users, fewer researchers consider the data security and privacy-preserving schemes. Therefore, we advocate the federated learning technique as an efficient and scalable method, where it enables the handling of a massive number of edge devices in parallel. In this study, we conduct the depression analysis on the basis of an online microblog called Weibo. A novel algorithm termed as CNN Asynchronous Federated optimization (CAFed) is proposed based on federated learning to improve the communication cost and convergence rate. It is shown that our proposed method can effectively protect users' privacy under the premise of ensuring the accuracy of prediction. The proposed method converges faster than the Federated Averaging (FedAvg) for non-convex problems. Federated learning techniques can identify quality solutions of mental health problems among Weibo users

Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder

The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches

Estimation of obesity levels based on computational intelligence

Obesity is a worldwide disease that affects people of all ages and gender; in consequence, researchers have made great efforts to identify factors that cause it early. In this study, an intelligent method is created, based on supervised and unsupervised techniques of data mining such as Simple K-Means, Decision Trees (DT), and Support Vector Machines (SVM) to detect obesity levels and help people and health professionals to have a healthier lifestyle against this global epidemic. In this research the primary source of collection was from students 18 and 25 years old at institutions in the countries of Colombia, Mexico, and Peru. The study takes a dataset relating to the main causes of obesity, based on the aim to reference high caloric intake, a decrease of energy expenditure due to the lack of physical activity, alimentary disorders, genetics, socioeconomic factors, and/or anxiety and depression. In the selected dataset, 178 students participated in the study, 81 male and 97 female. Using algorithms including Decision Tree, Support Vector Machine (SVM), and Simple K-Means, the results show a relevant tool to perform a comparative analysis among the mentioned algorithms

Evaluating Mental Stress Among College Students Using Heart Rate and Hand Acceleration Data Collected from Wearable Sensors

Stress is various mental health disorders including depression and anxiety among college students. Early stress diagnosis and intervention may lower the risk of developing mental illnesses. We examined a machine learning-based method for identification of stress using data collected in a naturalistic study utilizing self-reported stress as ground truth as well as physiological data such as heart rate and hand acceleration. The study involved 54 college students from a large campus who used wearable wrist-worn sensors and a mobile health (mHealth) application continuously for 40 days. The app gathered physiological data including heart rate and hand acceleration at one hertz frequency. The application also enabled users to self-report stress by tapping on the watch face, resulting in a time-stamped record of the self-reported stress. We created, evaluated, and analyzed machine learning algorithms for identifying stress episodes among college students using heart rate and accelerometer data. The XGBoost method was the most reliable model with an AUC of 0.64 and an accuracy of 84.5%. The standard deviation of hand acceleration, standard deviation of heart rate, and the minimum heart rate were the most important features for stress detection. This evidence may support the efficacy of identifying patterns in physiological reaction to stress using smartwatch sensors and may inform the design of future tools for real-time detection of stress