An analytical model for regular respiratory signal

In disaster rescue, breathing motion detection is an important approach to searching survivors trapped under debris. Detection of breathing motion is realized by detecting the respiratory signal acquired by the sensing system. In this paper, modeling the regular respiratory signal is studied. Firstly, a preliminary model is built based on power of absolute value of cosine function. Then, this preliminary model is improved in terms of some practical considerations, such as the DC-component of the respiratory signal often is removed by signal processing, and a phase uncertainty occurs due to the data acquisition. Finally, an analytical harmonic-based random respiratory signal model is derived, which can be used as the signal model in the future research about breathing motion detection.