title: Non-contact, synchronous dynamic measurement of respiratory rate and heart rate based on dual sensitive regions.
abstract: BACKGROUND
  Currently, many imaging photoplethysmography (IPPG) researches have reported non-contact measurements of physiological parameters, such as heart rate (HR), respiratory rate (RR), etc. However, it is accepted that only HR measurement has been mature for applications, and other estimations are relatively incapable for reliable applications. Thus, it is worth keeping on persistent studies. Besides, there are some issues commonly involved in these approaches need to be explored further. For example, motion artifact attenuation, an intractable problem, which is being attempted to be resolved by sophisticated video tracking and detection algorithms.
  
  
  METHODS
  This paper proposed a blind source separation-based method that could synchronously measure RR and HR in non-contact way. A dual region of interest on facial video image was selected to yield 6-channels Red/Green/Blue signals. By applying Second-Order Blind Identification algorithm to those signals generated above, we obtained 6-channels outputs that contain blood volume pulse (BVP) and respiratory motion artifact. We defined this motion artifact as respiratory signal (RS). For the automatic selections of the RS and BVP among these outputs, we devised a kurtosis-based identification strategy, which guarantees the dynamic RR and HR monitoring available.
  
  
  RESULTS
  The experimental results indicated that, the estimation by the proposed method has an impressive performance compared with the measurement of the commercial medical sensors.
  
  
  CONCLUSIONS
  The proposed method achieved dynamic measurement of RR and HR, and the extension and revision of it may have the potentials for more physiological signs detection, such as heart rate variability, eye blinking, nose wrinkling, yawn, as well as other muscular movements. Thus, it might provide a promising approach for IPPG-based applications such as emotion computation and fatigue detection, etc.
authors.affiliation: School of Computer Science and Technology, Anhui University, Hefei, 230601, China.|Department of Computer Science and Technology, Hefei Normal College, Hefei, 230061, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Wei
authors.affiliation: School of Computer Science and Technology, Anhui University, Hefei, 230601, China.
authors.forename: Xuan
authors.initials: X
authors.lastname: He
authors.affiliation: School of Computer Science and Technology, Anhui University, Hefei, 230601, China.
authors.forename: Chao
authors.initials: C
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Technology, Anhui University, Hefei, 230601, China. wxp2001@ahu.edu.cn.
authors.forename: Xiaopei
authors.initials: X
authors.lastname: Wu
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D005145:Face / Q000502:physiology; D005221:Fatigue / Q000175:diagnosis; D005260:Female; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D008991:Monitoring, Physiologic / Q000379:methods*; D009038:Motion; D017156:Photoplethysmography; D056152:Respiratory Rate / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D014741:Video Recording; D055815:Young Adult
keywords: BSS; Motion artifact; Power spectrum kurtosis; Respiratory rate; SOBI