Key result
Unscented Kalman filter improves heart rate estimation through multimodal sensor fusion.
Why the study?
Improving heart rate estimation by leveraging redundancy in multimodal cardiorespiratory sensor signals using model knowledge was needed.
Based on a model of three coupled oscillators describing the influence of respiration, namely respiratory sinus arrhythmia (RSA), and so-called Mayer waves on the heart rate, an unscented Kalman filter (UKF) is designed to perform sensor fusion of multimodal cardiorespiratory sensor signals. The aim is to implicitly use redundancy between the sensor signals to improve the estimated heart rate while utilizing model knowledge. The effectiveness of the approach is shown by estimations of heart rate variability on synthesized data as well as multimodal patient data, which provide different numbers of sensor channels.
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Linschmann et al. (2020) studied this question. Unscented Kalman filter (UKF) for sensor fusion was evaluated on Estimation of heart rate variability. An unscented Kalman filter designed for sensor fusion of multimodal cardiorespiratory signals effectively improved heart rate estimation using redundancy between sensor signals.
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