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

Aug 5, 2026

Upload by Patrick

More than 94% of adults recognize stress as a contributor to serious health problems, yet stress and anxiety remain difficult to measure accurately because they are subjective. This thesis experiments with recognizing and assessing stress and anxiety levels through a smartphone-based Ecological Momentary Assessment workflow. The STAND app prompts daily self-ratings plus selfie capture and collects sensor data, while optional video recordings from neuropsychological games support binary classification. Experiments further evaluate personalized emotion recognition using the Emognition dataset.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages73

Reading Time3 h 4 min

AccessFree to download

Introduction

Research Background and Significance

Literature Review

Ecological Momentary Assessment (EMA)

Conventional Approaches and Techniques

Facial Signs and Expressions

Logging Stress and Anxiety Using a Gamified Mobilebased EMA Application, and Emotion Recognition Using a Personalized Machine Learning Approach Smart Systems Master’s Degree Programme in Information and Communication Technology Department of Computing, Faculty of Technology Master of Science in Technology Thesis Author: Ali Kargarandehkordi Supervisors: Assistant Professor, Peter Yiğitcan Washington (University of Hawaiʻi at Mānoa, USA) Assistant Professor, Matti Kaisti (University of Turku, Finland) June 2023 The originality of this thesis has been checked in accordance with the University of Turku quality assurance system using the Turnitin Originality Check service. Master of Science in Technology Thesis Department of Computing, Faculty of Technology University of Turku Subject: Smart Systems Programme: Master’s Degree Programme in Information and Communication Technology Author: Ali Kargarandehkordi Title: Logging Stress and Anxiety Using a Gamified Mobile-based EMA Application, and Emotion Recognition Using a Personalized Machine Learning Approach Number of pages: 67 pages, 6 appendix pages Date: June 2023 Abstract. 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

FAQs

What is the STAND mobile application used for in this thesis?

STAND supports Ecological Momentary Assessment by delivering daily notifications that collect self-reported stress and anxiety ratings, selfie images, and sensor data.

How are stress and anxiety signals related to facial and temporal features?

The study analyzes relationships between self-reported stress/anxiety and facial indicators, including eye features (e.g., pupil size variation and eye closure) and specific action units across time.

Which models were tested for personalized machine-learning emotion recognition?

Three models—KNN, Random Forest, and MLP—were evaluated on the Emognition dataset, with reported accuracies of 93%, 95%, and 87% respectively.

Aug 5, 2026

Upload by Patrick