Chest compression rate measurement from smartphone video.
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...
| Publicado en: | BioMedical Engineering OnLine Vol. 15; pp. 1 - 20 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
8/11/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=117412434&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117412434 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 8/11/2016 vid: 15 pid: 24147 pub: BioMed Central artinfo: ui: 117412434 117412434 NLM27516194 10.1186/s12938-016-0218-6 NLM27516194 PMC4982121 117412434 ppf: 1 ppct: 19 formats: tig: atl: Chest compression rate measurement from smartphone video. aug: au: Engan, Kjersti Hinna, Thomas Ryen, Tom Birkenes, Tonje S. Myklebust, Helge affil: Department of Electrical and Computer Engineering, University of Stavanger, Stavanger, Norway sug: subj: Videorecording Resuscitation, Cardiopulmonary Thorax Mobile Applications Kinetics Female User-Computer Interface Male Feedback Clinical Assessment Tools Scales Short Portable Mental Status Questionnaire Female Male ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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