title: Chest compression rate measurement from smartphone video.
abstract: BACKGROUND
  Out-of-hospital cardiac arrest is a life threatening situation where the first person performing cardiopulmonary resuscitation (CPR) most often is a bystander without medical training. Some existing smartphone apps can call the emergency number and provide for example global positioning system (GPS) location like Hjelp 113-GPS App by the Norwegian air ambulance. We propose to extend functionality of such apps by using the built in camera in a smartphone to capture video of the CPR performed, primarily to estimate the duration and rate of the chest compression executed, if any.
  
  
  METHODS
  All calculations are done in real time, and both the caller and the dispatcher will receive the compression rate feedback when detected. The proposed algorithm is based on finding a dynamic region of interest in the video frames, and thereafter evaluating the power spectral density by computing the fast fourier transform over sliding windows. The power of the dominating frequencies is compared to the power of the frequency area of interest. The system is tested on different persons, male and female, in different scenarios addressing target compression rates, background disturbances, compression with mouth-to-mouth ventilation, various background illuminations and phone placements. All tests were done on a recording Laerdal manikin, providing true compression rates for comparison.
  
  
  RESULTS
  Overall, the algorithm is seen to be promising, and it manages a number of disturbances and light situations. For target rates at 110 cpm, as recommended during CPR, the mean error in compression rate (Standard dev. over tests in parentheses) is 3.6 (0.8) for short hair bystanders, and 8.7 (6.0) including medium and long haired bystanders.
  
  
  CONCLUSIONS
  The presented method shows that it is feasible to detect the compression rate of chest compressions performed by a bystander by placing the smartphone close to the patient, and using the built-in camera combined with a video processing algorithm performed real-time on the device.
authors: Department of Electrical and Computer Engineering, University of Stavanger, Stavanger, Norway. kjersti.engan@uis.no.
authors: Kjersti
authors: http://orcid.org/0000-0002-8970-0067
authors: K
authors: Engan
authors: Department of Electrical and Computer Engineering, University of Stavanger, Stavanger, Norway.|BI Builders, Sandnes, Norway.
authors: Thomas
authors: T
authors: Hinna
authors: Department of Electrical and Computer Engineering, University of Stavanger, Stavanger, Norway.
authors: Tom
authors: T
authors: Ryen
authors: Laerdal Medical, Stavanger, Norway.
authors: Tonje S
authors: TS
authors: Birkenes
authors: Laerdal Medical, Stavanger, Norway.
authors: Helge
authors: H
authors: Myklebust
mesh_terms: D016887:Cardiopulmonary Resuscitation; D005246:Feedback; D005260:Female; D006801:Humans; D007700:Kinetics; D008297:Male; D063731:Mobile Applications; D000068997:Smartphone; D013909:Thorax; D014584:User-Computer Interface; D014741:Video Recording
keywords: CPR; FFT; Motion analysis; Smartphone; Video processing