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

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

A review of biophysiological and biochemical indicators of stress for connected and preventive healthcare

Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Automatic emotion recognition in clinical scenario: a systematic review of methods

none4Automatic emotion recognition has powerful opportunities in the clinical field, but several critical aspects are still open, such as heterogeneity of methodologies or technologies tested mainly on healthy people. This systematic review aims to survey automatic emotion recognition systems applied in real clinical contexts, to deeply analyse clinical and technical aspects, how they were addressed, and relationships among them. The literature review was conducted on: IEEEXplore, ScienceDirect, Scopus, PubMed, ACM. Inclusion criteria were the presence of an automatic emotion recognition algorithm and the enrollment of at least 2 patients in the experimental protocol. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Moreover, the works were analysed according to a reference model to deeply examine both clinical and technical topics. 52 scientific papers passed inclusion criteria. Most clinical scenarios involved neurodevelopmental, neurological and psychiatric disorders with the aims of diagnosing, monitoring, or treating emotional symptoms. The most adopted signals are video and audio, while supervised shallow learning is mostly used for emotion recognition. A poor study design, tiny samples, and the absence of a control group emerged as methodological weaknesses. Heterogeneity of performance metrics, datasets and algorithms challenges results comparability, robustness, reliability and reproducibility.openPepa, Lucia; Spalazzi, Luca; Capecci, Marianna; Ceravolo, Maria GabriellaPepa, Lucia; Spalazzi, Luca; Capecci, Marianna; Ceravolo, Maria Gabriell

COMPUTE DEPRESSION AND ANXIETY AMONG STUDENTS IN PAKISTAN, USING MACHINE LEARNING

The worldwide mechanical advancement in medical services digitizes the copious information, empowering the guide of the different types of human science all the more precisely than conventional estimating strategies. AI (ML) has been certified as a productive approach for dissecting the enormous measure of information in the medical services area. ML strategies are being used in emotional well-being to anticipate the probabilities of mental problems and, subsequently, execute potential treatment results. In the speedy present-day world, mental medical problems like depression and anxiety have become exceptionally normal among the majority. In this paper, forecasts of depression and anxieties were made utilizing AI calculations. Depression and anxiety have become emergent hindrances in the lives of human beings. It not only disturbs their daily decorum but has also become a prominent cause for their downfall in health. All around the world people are getting affected by this mental disorder yet the majority of such cases lie between ages 18-25 making university-going students a prime target for such mental diseases. Though the mental health of university students is known globally as a momentous public health matter. Academicals, social depression, and anxieties are playing quite a negative role in university student’s life, especially in forms of mental illness like depression and anxiety. These mental health issues are becoming a major constraint on their studies and career. Hence, this research is being conducted to develop a technological solution for mentally distorted students. This paper analyzes depression and anxiety amongst university students by effectively utilizing the k-nn algorithm (a conspicuous technique for detecting and analyzing mental depression and anxiety) and providing a technical solution for this mental hindrance. The experimental results show up to 76.5% accuracy in results after using k-nn without PCA while the accuracy was increased up to 76.6% when the results were generated with PCA

How to Relax in Stressful Situations: A Smart Stress Reduction System

Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a reduction in the individual's health, well-being and socio-economic situation. Stress management application development for wearable smart devices is a growing market. The use of wearable smart devices and biofeedback for individualized real-life stress reduction interventions has received less attention. By using our unobtrusive automatic stress detection system for use with consumer-grade smart bands, we first detected stress levels. When a high stress level is detected, our system suggests the most appropriate relaxation method by analyzing the physical activity-based contextual information. In more restricted contexts, physical activity is lower and mobile relaxation methods might be more appropriate, whereas in free contexts traditional methods might be useful. We further compared traditional and mobile relaxation methods by using our stress level detection system during an eight day EU project training event involving 15 early stage researchers (mean age 28; gender 9 Male, 6 Female). Participants' daily stress levels were monitored and a range of traditional and mobile stress management techniques was applied. On day eight, participants were exposed to a 'stressful' event by being required to give an oral presentation. Insights about the success of both traditional and mobile relaxation methods by using the physiological signals and collected self-reports were provided

Signal Processing of Multimodal Mobile Lifelogging Data towards Detecting Stress in Real-World Driving

Stress is a negative emotion that is part of everyday life. However, frequent episodes or prolonged periods of stress can be detrimental to long-term health. Nevertheless, developing self-awareness is an important aspect of fostering effective ways to self-regulate these experiences. Mobile lifelogging systems provide an ideal platform to support self-regulation of stress by raising awareness of negative emotional states via continuous recording of psychophysiological and behavioural data. However, obtaining meaningful information from large volumes of raw data represents a significant challenge because these data must be accurately quantified and processed before stress can be detected. This work describes a set of algorithms designed to process multiple streams of lifelogging data for stress detection in the context of real world driving. Two data collection exercises have been performed where multimodal data, including raw cardiovascular activity and driving information, were collected from twenty-one people during daily commuter journeys. Our approach enabled us to 1) pre-process raw physiological data to calculate valid measures of heart rate variability, a significant marker of stress, 2) identify/correct artefacts in the raw physiological data and 3) provide a comparison between several classifiers for detecting stress. Results were positive and ensemble classification models provided a maximum accuracy of 86.9% for binary detection of stress in the real-world