Using an inertial navigation algorithm and accelerometer to monitor chest compression depth during cardiopulmonary resuscitation. Issue 9 (September 2016)

• Record Type: Journal Article
• Authors: Boussen, Salah; Ibouanga-Kipoutou, Harold; Fournier, Nathalie; Raboutet, Yves Godio; Llari, Maxime; Bruder, Nicolas; Arnoux, Pierre Jean; Behr, Michel
• Is Part Of: Medical engineering & physics. Volume 38:Issue 9(2016:Sep.)
• Page Start: 1028
• Page End: 1034
• Publication Date: 2016-09
• Subjects: CPR -- Cardiac arrest -- Feedback devices -- Accelerometer -- Kalman filter; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2016.05.001
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: We used a three axis accelerometer to monitor CPR depth. CPR depth was accurately retrieved using Kalman filter. Kalman filter algorithm allows CPR depth measurement without drift and accuracy. CPR depth measurement depends on the accelerometer location. Abstract: We present an original method using a low cost accelerometer and a Kalman-filter based algorithm to monitor cardiopulmonary resuscitation chest compressions (CC) depth. A three-axis accelerometer connected to a computer was used during CC. A Kalman filter was used to retrieve speed and position from acceleration data. We first tested the algorithm for its accuracy and stability on surrogate data. The device was implemented for CC performed on a manikin. Different accelerometer locations were tested. We used a classical inertial navigation algorithm to reconstruct CPR depth and frequency. The device was found accurate enough to monitor CPR depth and its stability was checked for half an hour without any drift. Average error on displacement was ±0.5 mm. We showed that depth measurement was dependent on the device location on the patient or the rescuer. The accuracy and stability of this small low-cost accelerometer coupled to a Kalman-filter based algorithm to reconstruct CC depth and frequency, was found well adapted and could be easily implemented.