Treffer: Development of a Raspberry Pi based cortisol detector

Cortisol concentration is a key biomarker for assessing stress response and immune function, making it an important indicator of anxiety levels. However, traditional detection methods, such as blood tests, are often costly and complex. This study presents a low-cost and user-friendly cortisol detector based on the Raspberry Pi platform. The device employs a Raspberry Pi 4B as the central processing unit, integrating a camera module, LED system, and LCD display. It captures images of cortisol test strips based on the lateral flow immunoassay (LFIA) method, offering a rapid and noninvasive detection approach. Python scripts control LED illumination and image acquisition, while OpenCV-based image processing extracts intensity data from control (C) and test (T) lines to quantify cortisol concentration. Results are displayed in real time on the LCD screen. The system’s affordability and ease of use make it suitable for clinical and home applications. Future improvements may enhance automation and enable cloud-based data storage, further promoting stress monitoring and health assessment. ; Master's degree