Towards a Real-Time Warning System for Detecting Inaccuracies in Photoplethysmography-Based Heart Rate Measurements in Wearable Devices

TL;DR

This paper introduces a real-time warning system for wearable devices that detects inaccuracies in photoplethysmography-based heart rate measurements, improving user awareness and trust in health monitoring.

Contribution

We developed a deep learning-based system that detects inaccurate HR readings in real-time using only the HR signal, enhancing transparency in wearable health devices.

Findings

Detected over 80% of inaccurate readings

Improved user awareness and trust in wearable devices

Demonstrated effectiveness with data from Polar and Garmin

Abstract

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.