@inproceedings{islambouli_towards_2025,
 abstract = {Wearable devices with photoplethysmography (PPG) sensors are widely used to monitor heart rate (HR), yet often suffer from accuracy

issues. Despite this, users typically receive no indication of potential measurement errors. We present a real-time warning system that

detects and communicates inaccuracies in PPG-derived HR, aiming to enhance transparency and trust. Using data from Polar and

Garmin devices, we trained a deep learning model to classify HR accuracy using only the derived HR signal. The system detected over

80% of inaccurate readings. By providing interpretable, real-time feedback directly to users, our work contributes to HCI by promoting

user awareness, informed decision-making, and trust in wearable health technology.},
 author = {Islambouli, Rania and Brunner, Marlene and Kumar, Devender and Sareban, Mahdi and Treff, Gunnar and Neudorfer, Michael and Niebauer, Josef and Bathke, Arne and Smeddinck, Jan},
 language = {en},
 pages = {10.18420/muc2025},
 publisher = {Gesellschaft für Informatik e.V.},
 title = {Towards a Real-Time Warning System for Detecting Inaccuracies in Photoplethysmography-Based Heart Rate Measurements in Wearable Devices},
 url = {https://dl.gi.de/handle/20.500.12116/46794},
 urldate = {2025-10-02},
 year = {2025}
}