Type:

• Bachelor Thesis Business Information Systems
• Master Thesis Business Information Systems

Status:

offered

Tutor:

• M.Sc. Cosima von Uechtritz

Abstract

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