Algorithm to improve accuracy of energy expended in a room calorimeter

Abstract

The whole-room indirect calorimeter is considered as important equipment for human energy expenditure measurement, but noise reduction in the system remains a challenge. A selective filtering method (SFM) was designed to improve the accuracy of the computation of O2 consumption rate ([Formula: see text]) and CO2 production rate ([Formula: see text]), based on two facts: (1) the rapid changes of [Formula: see text], [Formula: see text] and respiratory quotient (RQ) in human should be accompanied by physical activity; (2) the oxygen consumption and the carbon dioxide production should not be negative because living humans do not generate oxygen, nor consume carbon dioxide. The performance of SFM was compared with the moving average method, the central difference method and the wavelet de-noising method. The range of [Formula: see text] and [Formula: see text] in the empty room (the background noise) is reduced from -130.00-146.00ml/min to -26.00-24.00ml/min, and from -20.50-12.50ml/min to -3.99-4.19ml/min, by SFM. The background noise was added to simulated rectangular and sinusoidal signals that were used to evaluate the four methods over different time periods (64, 32, 16 and 8min). The highest signal-to-noise ratio and the lowest deviation were achieved by SFM. Abnormal metabolic rates and RQs were corrected and compensated with measurement accuracy of 98.51±0.3% for 24-h alcohol burning tests. The results of the study showed that SFM can significantly improve [Formula: see text] and [Formula: see text] measurements.