JMIR Publications

ABSTRACT

Background:

Wearable photoplethysmography (PPG) enables continuous ambulatory physiological monitoring; however, heart rate variability (HRV) estimation under free-living conditions remains challenging because of motion artifacts, signal instability, and heterogeneous activity-dependent physiological variability. In particular, robust subject-independent wearable HRV estimation without user-specific calibration remains insufficiently explored.

Objective:

This study investigated a context-aware wearable HRV estimation framework integrating signal-quality-aware and motion-aware contextual information for subject-independent ultra-short-term HRV estimation across heterogeneous daily activities.

Methods:

Wrist PPG and accelerometer signals from the publicly available PPG-DaLiA dataset were analyzed under subject-independent evaluation conditions. The proposed framework integrated PPG waveform representations with two forms of contextual information: (1) signal-quality-aware contextual features representing wearable signal reliability and (2) accelerometer-derived motion-aware contextual information representing ambulatory movement intensity. Unlike approaches relying on explicit activity labels, the proposed framework used lightweight contextual representations derived directly from wearable sensor signals. Four experimental configurations were compared: PPG-only, PPG with signal-quality-aware contextual information, PPG with motion-aware contextual information, and PPG with full context-aware information. SDNN was used as the primary HRV target.

Results:

Signal-quality and motion-related cues influenced wearable HRV estimation performance under subject-independent free-living conditions. The signal-quality-aware contextual configuration achieved the strongest overall estimation performance (Pearson r=0.548, MAE=0.0142 s, RMSE=0.0194 s), outperforming the PPG-only baseline configuration (Pearson r=0.329, MAE=0.0199 s, RMSE=0.0245 s). Motion-aware contextual information also improved overall trend tracking performance relative to the baseline model (Pearson r=0.444). However, contextual effects varied considerably across activities. Cycling and lunch break activities demonstrated relatively improved contextual estimation performance, whereas stair-related activities remained challenging across all configurations, with near-zero or negative correlation coefficients despite modest absolute estimation errors. Visualization and agreement analyses further demonstrated substantial differences in prediction behavior and estimation stability across contextual configurations.

Conclusions:

Contextual information substantially affected wearable HRV estimation robustness under heterogeneous free-living conditions. Signal-quality-aware contextual information demonstrated particularly consistent benefits across activities, whereas motion-aware contextual information showed more selective effects depending on ambulatory movement characteristics. These findings highlight the importance of contextual wearable sensing strategies for scalable calibration-free ambulatory physiological monitoring.