Feature Selection and Performance Analysis for Heart Rate Estimation from Smartphone Photoplethysmography

Abstract

Smartphone cameras enable continuous, equipment-free heart rate monitoring, yet the photoplethysmography (PPG) signals they capture are susceptible to movement, contact instability, and environmental variation that differ sharply from controlled validation settings. This study evaluated heart rate estimation from smartphone PPG signals using a feature-based approach, five classical regression models, and Recursive Feature Elimination (RFE) on the BUT PPG v2.0.0 dataset (3,888 recordings from 50 subjects, ECG reference). A total of 21 features were extracted from time, morphology, frequency, and signal quality domains, normalized using Yeo-Johnson, and evaluated under a subject-wise 5-fold cross-validation scheme. SVR with an RBF kernel achieved the best performance (MAE 9.20 bpm, RMSE 12.05 bpm). Feature selection reduced the feature count from 21 to 12 with negligible performance loss (MAE 9.19 bpm), and a Jaccard Stability Index of 0.7513 indicated that the selected subset generalizes consistently. Condition-stratified error analysis revealed that signal quality is the primary error driver, with MAE rising from 6.49 bpm on clean signals to 9.91 bpm on noisy ones, and that dynamic activities such as walking (13.69 bpm), laughing (12.74 bpm), and coughing (11.62 bpm) produce the highest errors. These findings indicate that upstream signal quality assessment is a necessary component of a reliable smartphone PPG heart rate estimation system.