Estimation of respiratory rate from smartphone's acceleration data

Abnormal respiratory rates have been shown to be an important predictor of serious clinical illness, but respiratory rate is a vital sign that is often not recorded because methods for measuring respiratory rates are cumbersome. We propose an approach to record and monitor respiratory rate of a patient that is lying down by placing an accelerometer-equipped smartphone on the patient's chest. We develop an algorithm based on fast Fourier transform (FFT) to estimate the respiratory rate from the noisy acceleration data. The main contribution of this paper is that our proposed algorithm can estimate respiratory rates using only tri-axial acceleration data from sensor in commodity smartphones without any other special equipment. Preliminary results show that our method can reasonably estimate the respiratory rate.