Bio:

Cosima v. Uechtritz is a research assistant at the Chair of Information Systems and Artificial Intelligence (AI) in Marketing at the University of Duisburg-Essen, Germany. During her time at the Fraunhofer Institute FKIE (2022 - 2025), she worked as a research assistant in the Human Factors research group with a focus on the assessment of cognitive states based on physiological parameters. She holds a Bachelor's degree in Human Movement Science from the University of Münster and a Master's degree in Human Technology from the German Sport University Cologne.

As a PhD student Cosima v. Uechtritz conducts research in the field of AI in Human Physiology.

Publications:

• von Uechtritz, Cosima; Nadj, Mario: Neurophysiological Correlates of Flow: A Systematic Review based on Csikszentmihalyi’s Flow Theory. In: Davis, Fred D.; Riedl, René; vom Brocke, Jan; Léger, Pierre-Majorique; Randolph, Adriane B.; Müller-Putz, Gernot R. (Ed.): Proceedings NeuroIS Retreat 2026. SpringerLink, Vienna, 2026. Details Full textCitation

Tutored Theses:

• A Machine Learning Approach to Flow State Classification in Gaming (Master Thesis Business Information Systems) Abstract

  This thesis investigates the automatic detection of flow states in gaming using machine learning techniques. Building upon existing research that links physiological signals, such as heart rate and heart rate variability, to the experience of flow, a classifier is developed and trained on a publicly available flow dataset. The trained model is then evaluated in a laboratory study, in which participants are measured repeatedly across several gaming sessions and receive feedback on their flow states during play. The study aims to advance understanding of flow in gaming and to explore the potential impact and benefits of flow feedback for players.

• Developing a Flow-adaptive System for E-Sports (Master Thesis Business Information Systems) Abstract

  Flow is the experience of being fully focused on an activity and fluidity of action. In this state, individuals often lose track of time and perform at their optimal levels. Thus, the flow state is of particular interest to e-sport players looking to enhance their performance. Therefore, technologies that are able to detect the current flow state of the user and help them enter or maintain this state could provide a valuable benefit, leading to improved performance. This thesis employs a design science research approach to develop a flow-adaptive system.

• Webcam-Based Flow Detection: A Machine Learning Approach Using Facial and Head Activity (Bachelor Thesis Business Information Systems) Abstract

  This thesis investigates the automatic detection of flow states using machine learning techniques applied to webcam data. Building upon existing research, a classifier is developed and trained on video recordings collected in a controlled experience sampling field study. The trained model is then evaluated on its ability to recognize flow states from unobtrusive webcam input, offering a feasible alternative to traditional measurement approaches. The thesis aims to advance the understanding of flow detection using webcam data.

• The Art of Feature Engineering: Comparing Hand-Crafted and Learned Features for Flow State Classification (Bachelor Thesis Business Information Systems) Abstract

  Flow, the state of optimal experience and complete absorption in an activity, is of growing interest in information systems research. Recent studies have shown that flow states can be classified using machine learning models trained on physiological data, such as heart rate and heart rate variability (HRV). For instance, Rissler et al. (2020) trained a flow classifier using a random forest model and achieved an accuracy of 70%. Traditional machine learning approaches often rely on hand-crafted features (HCFs), such as standard HRV metrics like SDNN or RMSSD. However, these features require expert knowledge and are labor-intensive to compute. Feature learning methods, such as deep neural networks, present a promising approach to overcome these limitations due to their capability to automatically extract relevant features. Therefore, feature learning approaches may outperform HCFs, in particular when dealing with large-scale, noisy, or unstructured data. 

  The aim of this thesis is to investigate the differences between HCFs and feature learning approaches for classifying flow states from physiological signals. Students working on this project will have access to a publicly available flow dataset.

  Rissler, R., Nadj, M., Li, M. X., Loewe, N., Knierim, M. T., & Maedche, A. (2020). To be or not to be in flow at work: physiological classification of flow using machine learning. IEEE transactions on affective computing, 14(1), 463-474.

• The Academic Version of Apple Health: Build your own Wearable Research App (Bachelor Thesis Business Information Systems) Abstract

  Over the past years, consumer wearables have evolved from lifestyle gadgets into advanced sensing platforms capable of continuous physiological monitoring. Ecosystems like Garmin and Apple Health provide access to health metrics (e.g., sleep score) and raw data (e.g., heart rate) for research institutions. This is of particular importance for researchers interested in predicting diseases (e.g., hypertension) or classifying mental states (e.g., stress or flow), based on physiological data. Among these, flow presents a state of deep task engagement and optimal experience, and can be assessed with physiological indicators, such as heart rate variability (HRV) or respiration rate.

  Therefore, the aim of this thesis is to develop a Garmin application that records physiological data and correlates it with user-reported flow experiences. Students will receive access to the Garmin Developer Portal and will implement a mobile application capable of collecting HRV-related metrics and questionnaire-based self-reports. The application will then be evaluated in a small pilot study to explore relationships between health metrics and flow data.

  • Henriksen, A., Haugen Mikalsen, M., Woldaregay, A. Z., Muzny, M., Hartvigsen, G., Hopstock, L. A., & Grimsgaard, S. (2018). Using fitness trackers and smartwatches to measure physical activity in research: analysis of consumer wrist-worn wearables. Journal of medical Internet research, 20(3), e110.

• Quantifying Flow – A Systematic Review and Evaluation of Flow Questionnaires (Bachelor Thesis Business Information Systems) Abstract

  The theory of flow, which describes the state of being completely absorbed into an activity, has gained considerable attention in recent years across a wide range of domains. As a result, numerous questionnaires have been developed to assess the experience of flow and to account for these different contexts, such as “the flow state scale for occupational tasks” or the “reading flow short scale”. However, the use of a wide variety of questionnaires across studies makes it difficult to compare and synthesize findings and therefore hinders the development of flow research. 

  Therefore, the aim of this thesis is to systematically identify existing flow questionnaires and assess their methodological validity. Based on this analysis, a framework will be developed to guide the selection and application of flow questionnaires depending on the research context and purpose.

  • Rosas, D. A., Padilla-Zea, N., & Burgos, D. (2023). Validated questionnaires in flow theory: a systematic review. Electronics, 12(13), 2769.

• Towards Flow-Aware E-Sports Coaching (Bachelor Thesis Business Information Systems) Abstract

  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 coaches and institutions in competitive e-sports to support individual and team performance. A system capable of detecting flow in e-sports players could provide coaches and institutions with insights to help players enter or remain in this state. 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 coaches and representatives of esports institutions, and translate their findings into corresponding design guidelines. Finally, the resulting requirements inform the development of a first prototype system.

• A Flow-Specific Task Classification Framework (Bachelor Thesis Business Information Systems) Abstract

  Flow describes a state of complete absorption in a task, which occurs when an individual's perceived skills match the perceived challenge of the task. Because flow is related to increased performance and well-being, it has been studied across multiple domains such as knowledge work, sports, and gaming. However, research indicates that the flow experience differs depending on the characteristics of the task. Yet no framework systematically classifies tasks according to their flow-relevant characteristics.

  Thus, the aim of this thesis is to develop a framework that classifies tasks according to characteristics relevant to the flow theory. Students derive flow-relevant task characteristics from existing research and consolidate them into a classification framework.

• Remote Vital Sign Monitoring Using Smart Cameras: An Exploration of Use Cases and User study in the Context of Knowledge Workers (Bachelor Thesis Business Information Systems) Abstract

  Smart camera-based monitoring systems are capable of tracking and monitoring human activities. In recent years, systems have been developed that enable contactless monitoring of vital signs such as heart rate and oxygen saturation, capture human behavior such as posture and eye activity, and are able to detect human emotions. In contrast to other monitoring systems, cameras have the advantage of being low-cost and unobtrusive, which facilitates their implementation in different environments. Despite the technological capabilities of these systems, their use in the real world is currently limited. 

  Therefore, the aim of this Bachelor thesis is to identify use cases and their potential in real-life scenarios. In order to successfully complete this Bachelor thesis, use cases will first be identified through a systematic literature review and then their potential will be explored through an online survey (e.g. LimeSurvey).

• Exploring the Value of PPG-based Wearables in Digital Health: From Literature to Implementation (Bachelor Thesis Business Information Systems) Abstract

  Modern smartwatches and smart rings increasingly rely on photoplethysmography (PPG) algorithms to measure vital signs such as heart rate, sleep patterns and stress levels. These sensors are particularly attractive because they are non-invasive and inexpensive, which makes them suitable for integration into everyday life. In recent years, advances in signal processing and machine learning have significantly improved the ability to extract meaningful insights from raw PPG data. For example, the first commercially available smartwatches are now able to measure blood pressure, offering an exciting solution for health monitoring. However, most commercial consumer devices do not allow researchers and individuals to access the raw PPG signal, significantly limiting the potential for innovation.

  The aim of this thesis is to first define the potential of PPG-based sensors through a systematic literature review. Subsequently, a prototype will be developed that enables the streaming of raw PPG data in real time.

  Note: A PPG sensor is provided for technical implementation.

• How can rPPG algorithms be improved to achieve more robust heart rate estimation under motion and illumination variability? (Bachelor Thesis Business Information Systems) Abstract

  Contactless vital parameter estimation, such as heart rate, has gained substantial relevance in recent years, as it enables unobtrusive monitoring with widely available devices, such as webcams or smartphone cameras. Within this domain, remote photoplethysmography (rPPG) has emerged as a promising technique. However, the practical applicability of rPPG is still limited by critical sources of error, such as motion artifacts and illumination variability. Both effects reduce the reliability of parameter estimation in real-world conditions. Therefore, the thesis aims to explore how the robustness of rPPG-based heart-rate estimation can be improved under motion and illumination variability.

• Modular Webcam-Based Architecture for Multi-Parameter Sensing (Bachelor Thesis Business Information Systems) Abstract

  Desk work associated with prolonged sitting and visual strain is linked to musculoskeletal complaints and reduced well-being. Existing solutions address these issues, but typically focus on a single function, such as posture reminders or micro-break prompts. The aim of this thesis is therefore to develop a webcam-based sensing system able to support the well-being of desk workers. To address this gap, an exploratory literature review to derive requirements for a modular, privacy-preserving architecture is conducted. On this basis, a system is built that can simultaneously measure posture, gaze, and blink rate, in addition to heart rate via remote photoplethysmography, to provide the user with feedback.

• Design and Evaluation of a Flow-Adaptive System for Software Developers (Bachelor Thesis Business Information Systems) Abstract

  Flow is the experience of being fully focused on an activity and the fluidity of action. In this state, individuals often lose track of time and perform at their optimal levels. For software developers whose productivity relies on attention and engagement in the task, entering a flow state can improve their work efficiency and well-being. Therefore, technologies that help software developers to enter a flow state could provide a valuable benefit, leading to reduced mental workload and improved performance