Key result
A robust estimate of breathing rate can be derived by fusing the outputs of independent Kalman filters from multiple sensors using a weighting calculated from squared differences.
Multi-sensor fusion using Kalman filters provides a robust method for estimating breathing rate from various physiological signals.
May enable reliable multisensor breathing rate monitoring; leaves open prospective clinical validation before adoption.
Respiratory information can be obtained from the changes in electrical impedance across the chest, the electrocardiogram or the changes in light absorption across the finger. Running estimates of the breathing rate from each of these can be obtained from independent Kalman filters. A robust estimate of breathing rate is derived by fusing the outputs of these independent Kalman filters using a weighting calculated from the squared differences between each prediction from the filters and the corresponding measurements.
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Tarassenko et al. (2002) studied this question. Multi-sensor fusion using Kalman filters was evaluated on Robust estimate of breathing rate. A robust estimate of breathing rate can be derived by fusing the outputs of independent Kalman filters from multiple sensors using a weighting calculated from squared differences.
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