Recent literature has called for the development of flow-adaptive systems. Flow, defined as being completely absorbed in a task, is linked to improved performance and well-being, so a system that detects this state and adapts to the needs of a user could help to reach and sustain it. The success of such a system depends on its ability to classify flow non-intrusively and reliably. Most current approaches rely on machine learning models trained on physiological data, such as electrocardiogram (ECG) data. These models require large datasets, and their accuracies range between 63% and 73%. An alternative approach is rule-based classification, in which decision rules are defined top-down based on identified associations between physiological data and flow. The aim of this thesis is to conduct a systematic literature review identifying relationships between flow and physiological data and, based on these findings, design three rule-based classification approaches. These approaches are then evaluated in an experimental study.

Flow describes a state of complete absorption in a task, which can be experienced across various activities such as learning, music, or gaming. This state can be captured through questionnaires or physiological measurements such as heart rate or brain activity. Because flow is related to increased performance and well-being, it is of high interest to a range of stakeholders in the gaming industry. Such as trainers and players aiming to improve individual and team performances, or game designers seeking to increase engagement. A system capable of detecting flow in e-sports players and supporting them in entering or remaining in it could therefore benefit all of these stakeholders. Yet no such flow-supportive system exists in the context of competitive e-sports.
Thus, the aim of this thesis is to develop an artefact for a physiological flow system in a competitive esports environment. Students identify requirements through a systematic literature review and interviews with representatives from different stakeholder groups, and translate their findings to corresponding design guidelines. Finally, the resulting guidelines are evaluated in a second round of interviews.

Over the past years, remote photoplethysmography (rPPG) algorithms have evolved into powerful tools capable of extracting cardiac features from standard webcams. Among these, heat rate and, in particular, heart rate variability are of importance for researchers interested in classifying mental states (e.g., stress or flow). For new technologies like rPPG to be integrated into research projects, the valid and reliable measurement of these parameters is decisive. While the first commercial providers already offer rPPG solutions with medical-grade certification for remote heart rate estimation, the measurement of HRV remains more challenging, and transparent information on its accuracy is often unavailable. An independent evaluation against an established reference standard is therefore needed.

Thus, the aim of this thesis is to derive cardiac features from a commercial rPPG algorithm (students get access to the license once the thesis is registered) and evaluate them against a synchronized ECG gold standard.

One promise of artificial intelligence (AI) in medicine is to enable learning from the vast amounts of observational data collected at diverse medical institutions. With all the benefits for knowledge generation and more refined decision making this may bring, it leaves a problem of scientific rigor. To a large degree, medical decision making is bounded by guidelines based on evidence from randomized clinical trials (RCT). However, this trial data might not cover each and every combination of patient characteristics, treatment options, and individual circumstance. Moreover, updating guidelines takes time and the wealth of new literature being published is prone to overwhelming practitioners. A solution to these problems could be found in retrieval augmented generation (RAG) for medical studies. Combing such systems with a clinical decision support system (CDSS) would lead to enhanced explainability, and an improved capacity to assess the quality of AI advice. 

In this thesis project, the student will develop and implement an RAG system for medical treatment decisions in the domain of gastroenterology in three phases. First, a scoping review of RAG in medicine will be conducted alongside a series of interviews with RAG experts and gastroenterologists for requirements elicitation. Then, the system will be implemented for studies on chronic inflammatory bowel disease. Finally, the student conducts an interview study with gastroenterologists to evaluate the system. The interviews will be recorded, transcribed, and analyzed (e.g., via tools like MAXQDA).

The digital health market is increasingly moving from a niche market to a mainstream market, and is expected to grow at an annual growth rate of 5.42% to reach a projected market volume of USD 219.60 billion by 2030 (Statista Market Insights, 2025). Health monitoring is an important sub-segment within the digital health market. 

Recent advances in artificial intelligence have significantly improved the accuracy of remote photoplethysmography (rPPG) algorithms. Using these algorithms, heart rate and other vital signs can be measured using a standard RGB camera, enabling completely contactless health monitoring. While heart rate can already be assessed with relatively high accuracy, the reliable extraction of heart rate variability, an important indicator of mental states, remains an ongoing challenge. 

Therefore, the aim of this thesis is to develop and validate an AI-based rPPG algorithm for heart rate variability extraction. An open access dataset (e.g., DEAP, MAHNOB-HCI) will be used to train and develop the algorithm. In addition, a small data sample will be collected using a reference measurement device (e.g., ECG chest strap) for validation purposes. The resulting data will then be compared and evaluated using selected performance indicators (e. g. mean absolute error, Pearson correlation coefficient).

• Statista Market Insights (2025). Digital Health. Statista. www.statista.com/outlook/hmo/digital-health/worldwide Retrieved 08.03.2025