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.

Tutored Theses:

• Smart Camera-based Human Monitoring Systems: An Exploration of Use Cases in Applied Research (Bachelor Thesis Business Information Systems)

  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)

  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.

• Development and Evaluation of an AI-Based Algorithm in the field of Digital Health (Bachelor Thesis Business Information Systems)

  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 6.88% to reach a projected market volume of USD 258.25 billion by 2029 (Statista Market Insights, 2024). Health monitoring is an important sub-segment within the digital health market. Smartwatches or smart rings that monitor heart rate or other fitness metrics are already widely used by people for various reasons.

  However, these devices have certain drawbacks as they require direct skin contact and are often very expensive. As a result, modern solutions for contactless and cost-effective health monitoring have gained considerable attention in recent years. In particular, new potential is emerging in areas such as road accident prevention and telemedicine, where non-invasive solutions are particularly valuable.

  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.

  The aim of this work is to develop and validate an AI-based rPPG algorithm for real-time heart rate extraction. A prepared test dataset (UBFC-Phys) 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 (MAE), Pearson correlation coefficient).

  Statista Market Insights (2024). Digital Health. Statista. www.statista.com/outlook/hmo/digital-health/worldwide Retrieved 08.05.2025