Assessing photoplethysmography signal quality for wearable devices during unrestricted daily activities

Photoplethysmography (PPG) is widely used in wearable health monitors for tracking fundamental physiological parameters (e.g., heart rate and blood oxygen saturation) and advancing applications requiring high-quality signals-such as blood pressure assessment and cardiac arrhythmia detection. However, motion artifacts and environmental noise significantly degrade the accuracy of PPG-derived physiological measurements, potentially causing false alarms or delayed diagnoses in longitudinal monitoring cohorts. While signal quality assessment (SQA) provides an effective solution, existing methods show insufficient robustness in ambulatory scenarios. This study concentrates on PPG signal quality detection and proposes a robust SQA algorithm for wearable devices under unrestricted daily activities. PPG and acceleration signals were acquired from 54 participants using a self-made physiological monitoring headband during daily activities, segmented into 35712 non-overlapping 5-second epochs. Each epoch was annotated with: (1) PPG signal quality levels (good: 10817; moderate: 14788; poor: 10107), and (2) activity states classified as sedentary, light, moderate, or vigorous-intensity. The dataset was stratified into training (80%) and testing (20%) subsets to maintain proportional representation. Fourteen discriminative features were extracted from four domains: morphological characteristics, time-frequency distributions, physiological parameters estimation consistency and accuracy, and statistical properties of signal dynamics. Four machine learning algorithms were employed to train models for SQA. The random forest (95.6%) achieved the highest accuracy on the test set, but no significant differences (p = 0.471) compared to support vector machine (95.4%), naive Bayes (94.1%), and BP neural network (95.1%). Additionally, the classification accuracy showed no statistically significant variations (p = 0.648) across light (95.3%), moderate (96.0%), and vigorous activity (100%) when compared to sedentary (95.8%). All features exhibited significant differences (p < 0.05) across high/moderate/poor quality segments in all pairwise comparisons.The results indicate that the proposed feature set achieves robust SQA, maintaining consistently high classification accuracy across all activity intensities. This performance stability enables real-time implementation in wearable devices.