Key result
PPG sensor accuracy drops during high-intensity exercise, showing ~18% error versus ECG.
Why the study?
The study was conducted to critically examine the validity and limitations of wrist-worn photoplethysmography sensors in classifying exercise intensity zones in real time across different intensity domains.
Do wrist-worn photoplethysmography (PPG) sensors accurately classify exercise intensity zones in real time compared to ECG?
Systematic Review
Do wrist-worn photoplethysmography (PPG) sensors accurately classify exercise intensity zones in real time compared to ECG?
Wrist-worn PPG sensors show significant declines in accuracy at high exercise intensities, raising concerns for their use in zone-based training prescriptions.
Caution against PPG-guided high-intensity zone training; confirms motion artifact limits and supports hybrid validation studies.
Purpose This paper aims to critically examine the validity of wrist-worn photoplethysmography (PPG) sensors in classifying exercise intensity zones in real time, with particular emphasis on the interplay between physiological, biomechanical and technological factors. This review addresses limitations in validity across different intensity domains and explores solutions integrating advanced analytics and signal processing. Design/methodology/approach Over 100 peer-reviewed sources were systematically reviewed to assess the validity and limitations of PPG sensors across treadmill, cycling and free-living conditions. The review dissects validity by intensity zones, explores inter-subject variability linked to skin pigmentation, BMI and age and evaluates algorithmic approaches from simple averaging filters to convolutional and recurrent neural networks. Findings Validity of PPG sensors significantly declines with increasing exercise intensity, primarily due to motion artefacts, vasoconstriction and low signal-to-noise ratios. Variability across devices and user demographics remains a major challenge. Studies show that hybrid sensor configurations (e.g. IMU + PPG) and machine learning-based artefact rejection (CNN–LSTM) enhance signal fidelity. However, most commercial wearables still rely on proprietary heuristics, limiting transparency and reproducibility. Cross-validation against ECG gold standards reveals that mean absolute percentage error increases up to 18% in high-intensity domains, raising concerns for zone-based training prescriptions. Originality/value This review bridges engineering, physiology and data science by offering a comprehensive synthesis of the mechanisms, limitations and solutions associated with PPG-based HR sensing in sports. It critiques the sufficiency of existing validation frameworks and advocates for standardised benchmarking, federated data sets and interpretable AI to guide future innovation.
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Yange Jiao (2026) conducted a systematic review in Exercise intensity zones. Wrist-worn photoplethysmography (PPG) sensors vs. ECG gold standards was evaluated on Mean absolute percentage error in high-intensity domains. Wrist-worn photoplethysmography sensors show declining validity during high-intensity exercise, with mean absolute percentage error increasing up to 18% compared to ECG gold standards.
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