Nir Regev

Nir Regev

Principal Algorithms Engineer, CTO’s Office

Essence Group


Nir is a researcher with Essence Group CTO’s office, responsible for bringing to life future technologies for the company. Nir’s research interests are statistical signal processing, namely, estimation, detection theory and machine learning applied to problems in computer vision and radar remote sensing. Nir is also a Ph.D. student in Ben-Gurion University, researching the problem of radar remote sensing, classification and recognition of miniature drones. 

Title:A Simple, Remote, Video Based Breathing Monitor

Breathing monitors have become the all-important cornerstone of a wide variety of commercial and personal safety applications, ranging from elderly care to baby monitoring.  Many such monitors exist in the market, some, with vital signs monitoring capabilities, but none remote. We presents a simple, yet efficient, real time method of extracting the subject's breathing sinus rhythm. Points of interest are detected on the subject's body, and the corresponding optical flow is estimated and tracked using the well-known Lucas-Kanade algorithm on a frame by frame basis. A generalized likelihood ratio test is then utilized on each of the many interest points to detect which is moving in harmonic fashion. Finally, a spectral estimation algorithm based on Pisarenko harmonic decomposition tracks the harmonic frequency in real time, and a fusion maximum likelihood algorithm optimally estimates the breathing rate using all points considered. The results show a maximal error of $1$ BPM between the true breathing rate and the algorithm's calculated rate, based on experiments on two babies and three adults.

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