Towards a Real-Time Warning System for Detecting Inaccuracies in Photoplethysmography-Based Heart Rate Measurements in Wearable Devices
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915466199433216 |
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| author | Islmabouli, Rania Brunner, Marlene Kumar, Devender Sareban, Mahdi Treff, Gunnar Neudorfer, Michael Niebauer, Josef Bathke, Arne Smeddinck, Jan David |
| author_facet | Islmabouli, Rania Brunner, Marlene Kumar, Devender Sareban, Mahdi Treff, Gunnar Neudorfer, Michael Niebauer, Josef Bathke, Arne Smeddinck, Jan David |
| contents | Wearable devices with photoplethysmography (PPG) sensors are widely used to monitor heart rate (HR), yet often suffer from accuracy issues. However, users typically do not receive an 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19818 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards a Real-Time Warning System for Detecting Inaccuracies in Photoplethysmography-Based Heart Rate Measurements in Wearable Devices Islmabouli, Rania Brunner, Marlene Kumar, Devender Sareban, Mahdi Treff, Gunnar Neudorfer, Michael Niebauer, Josef Bathke, Arne Smeddinck, Jan David Human-Computer Interaction Wearable devices with photoplethysmography (PPG) sensors are widely used to monitor heart rate (HR), yet often suffer from accuracy issues. However, users typically do not receive an 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. |
| title | Towards a Real-Time Warning System for Detecting Inaccuracies in Photoplethysmography-Based Heart Rate Measurements in Wearable Devices |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.19818 |