Detecting Chest Compression Depth Using a Smartphone Camera and Motion Segmentation

Meinich-Bache, Øyvind; Engan, Kjersti; Eftestøl, Trygve; Austvoll, Ivar

doi:10.1007/978-3-319-59129-2_5

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…We are currently investigating camera-based methods for measurement of compression depths [44]. In future work, we will try to develop a robust depth algorithm that could be implemented together with the proposed method.…”

Section: Discussionmentioning

confidence: 99%

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Real-Time Chest Compression Quality Measurements by Smartphone Camera

Meinich-Bache

Engan

Birkenes

et al. 2018

Journal of Healthcare Engineering

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Out-of-hospital cardiac arrest (OHCA) is recognized as a global mortality challenge, and digital strategies could contribute to increase the chance of survival. In this paper, we investigate if cardiopulmonary resuscitation (CPR) quality measurement using smartphone video analysis in real-time is feasible for a range of conditions. With the use of a web-connected smartphone application which utilizes the smartphone camera, we detect inactivity and chest compressions and measure chest compression rate with real-time feedback to both the caller who performs chest compressions and over the web to the dispatcher who coaches the caller on chest compressions. The application estimates compression rate with 0.5 s update interval, time to first stable compression rate (TFSCR), active compression time (TC), hands-off time (TWC), average compression rate (ACR), and total number of compressions (NC). Four experiments were performed to test the accuracy of the calculated chest compression rate under different conditions, and a fifth experiment was done to test the accuracy of the CPR summary parameters TFSCR, TC, TWC, ACR, and NC. Average compression rate detection error was 2.7 compressions per minute (±5.0 cpm), the calculated chest compression rate was within ±10 cpm in 98% (±5.5) of the time, and the average error of the summary CPR parameters was 4.5% (±3.6). The results show that real-time chest compression quality measurement by smartphone camera in simulated cardiac arrest is feasible under the conditions tested.

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Section: Discussionmentioning

confidence: 99%

“…More details can be found in [31, 36]. The application is called TCPR link and is available on App Store [43] and Google Play [44]. Let the input, f l ( i , j ), represent video frames, l , where ( i , j ) corresponds to row index i and column index j .…”

Section: Methodsmentioning

confidence: 99%