Type:

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

Status:

offered

Tutor:

• M.Sc. Cosima von Uechtritz

Abstract

Health tracking with smartwatches or fitness trackers for personalized health management and self-optimization has become increasingly popular. Today, around 260.7 million users track their steps, heart rate, stress levels and other parameters on a daily basis (Statista Health Market Insights, 2024). However, many of these self-tracking solutions rely on invasive devices that require direct skin contact and are often high in cost. A promising alternative is remote health tracking via camera, which could open up new possibilities. For example, a health tracker integrated into the computer camera could be synchronized with a digital calendar, allowing meetings to be scheduled and rescheduled based on the current stress level.

AI-based remote photoplethysmography algorithms are an innovative approach that enables contactless health monitoring using standard, low-cost cameras. A critical step in this process is the identification of specific areas of the face, known as region of interest (ROI), such as the forehead or cheeks. Stable tracking of the ROI is essential for extracting accurate and reliable heart rate signals. However, influencing factors, such as different lighting conditions, head movements, camera angle and position, make it difficult to obtain reliable measurements in real-world conditions. 

The aim of this thesis is to investigate and compare open source methods for real-time face and ROI recognition. First, common frameworks will be identified through a systematic literature review, and then a prototype will be implemented to evaluate them under selected influencing factors.

Statista Health Market Insights (2024). Statista Health Market Insights. de.statista.com/statistik/daten/studie/1460774/umfrage/nutzer-von-fitnesstrackern-weltweit/. Retrieved 08.05.2025