DSpace 7
DSpace is the world leading open source repository platform that enables organisations to:
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Recent Submissions
Konung Oscar I:s politik under Krimkriget /
(1930) Hallendorff, Carl,
Lärobok i Sveriges, Norges och Danmarks historia : för skolans högre klasser /
(1869) Odhner, Clas Theodor,
Balancing Fun and Function: Exploring Game Design Features for Long-Term Engagement in Anxiety-Relief Serious Games
(2025-10-09) Schröter, Pia; Göteborgs universitet/Institutionen för data- och informationsteknik; University of Gothenburg/Department of Computer Science and Engineering
Anxiety disorders are one of the most common psychological disorders, yet many people who are experiencing anxiety-related symptoms often remain undetected and untreated. To address this issue, more accessible and engaging solutions, such as serious games, are needed. This thesis explores how psychological practices for treating anxiety can be integrated with game design features that promote replayability without compromising therapeutic value. Various established therapeutic methods and game design features have been explored in order to create an effective mobile prototype. The final prototype featured three mini-games based on breathing exercises, relaxation-based exercises, and attention bias modification. It also incorporated additional features such as an in-game store, a streak system, a scoreboard and more to enhance user engagement. A playtest with 22 people was conducted to evaluate the prototype’s effectiveness in reducing anxiety and promoting continued use. Results indicated a reduction in self-reported anxiety and stress levels, and high user interest in replaying the prototype. Feedback from the testers highlighted the importance of customisation, as responses to the mini-games varied depending on individual preferences. The findings suggest that customisation and a diverse range of therapeutic content are key to supporting both anxiety relief and user engagement. Further research is needed to assess the prototype’s long-term impact.
Characteristic Stripe Pattern Masks Creation for Driving Maneuvers Identification Using Synthetic Data
(2025-10-09) Myroshnychenko, Svitlana; Göteborgs universitet/Institutionen för data- och informationsteknik; University of Gothenburg/Department of Computer Science and Engineering
Previous research has demonstrated that encoding n-dimensional driving data using space-filling curves reveals visual patterns that we call CSPs, which repeat among trajectories of the same type (e.g., roundabout passings, turns, braking, etc.). Unlike traditional methods, which rely on a limited number of real data samples to manually create binary CSP-masks for event identification, our approach systematically creates CSP-masks based on synthetic data. With this, we can explore in a structured way combination of factors such as speed, acceleration, and more that may help to determine the performance of software components, which process multi-dimensional, time-series data for pattern identification, to support testing of software components. To systematically create these masks for event identification, we compile the CSPs for each type of maneuver and cluster occurring stripes (same as clustering the indices).
Machine Learning on the football field: Predicting match performance through GPS data
(2025-10-09) Alvarado Pachón, Natalia; Schelling, Laura; Göteborgs universitet/Institutionen för data- och informationsteknik; University of Gothenburg/Department of Computer Science and Engineering
This master thesis evaluates machine learning models for predicting football match performance from GPS-based practice data. The growing use of GPS-based wearable tracking in professional football underscores the need for approaches that transform large datasets into practical insights for teams, while also contributing new methods and strategies to the scientific literature. Random Forests (RF), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) are tested on targets representing aerobic capacity (endurance in sustained activity), anaerobic capacity (ability to perform high-intensity efforts without oxygen), and explosiveness (short bursts of speed and power), using both non-overlapping rolling and adaptive feature windows. Three prediction strategies are compared: row-to-row, where each input window is paired with its corresponding target window; all-input-to-row, where the entire input sequence is used to predict each target row independently; and all-inputplus- previous-row to-row, which extends the second strategy by incorporating the previous target as an additional input. The results show that all models outperform linear regression in the last two strategies, with RF performing best for aerobic and anaerobic metrics and CNN and RNN for explosiveness. RF also provides feature importance scores, indicating that linear acceleration from the practice day immediately preceding the match is the strongest predictor. Angular velocity and angular jerk from the fourth and third practice days before the match also emerge as key factors, suggesting that strenuous training loads in the days leading up to competition may play a decisive role in match performance. CNN and RNN, in contrast, function as black-box models and do not directly provide interpretable insights into the relative importance of input features. Regarding windowing techniques, adaptive windowing reduces the Root Mean Squared Error (RMSE), which highlights a possible gain from moving into this approach for sport analysis. These findings offer practical insights for sports training and show how machine learning can turn wearable sensor data into useful performance metrics.