Accuracy improvement for heart rate monitoring from bed-based ballistocardiography signals with motion artifacts.

BackgroundBallistocardiography (BCG)-based heart rate (HR) monitoring faces accuracy degradation due to motion artifacts, limiting its practical deployment.ObjectiveThis study aims to enhance HR estimation reliability under motion-contaminated conditions while ensuring real-time performance.MethodsA hybrid system integrating adaptive filtering and enhanced continuous wavelet transform (CWT) is developed. The framework localizes motion segments (95.1% accuracy) and employs spectral reconstruction via magnitude-frequency nullification to restore HR from contaminated windows. Computational latency was evaluated on an embedded ARM platform to verify real-time feasibility.ResultsValidation using 6000 min of data demonstrated that the proposed method achieved an MAE of 2.94 BPM, comparable to the CNN-LSTM baseline (2.85 BPM), while reducing the average processing latency from 450.2 ms to 86.4 ms. Compared with conventional methods, the proposed framework reduced the MAE by 53.8% and improved monitoring stability by 35.6%. Bland-Altman analysis confirmed limits of agreement within [-4.77, 5.23] BPM, validating clinical reliability.ConclusionsThe proposed hybrid framework provides a computationally efficient and accurate solution for non-contact HR monitoring in motion-prone and bed-based clinical environments.