• Sarah R Ward, Bronwyn C Scott, David M Rubin, and Adam Pantanowitz.
• Biomedical Engineering Research Group, School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, Private Bag 3, 2050 Johannesburg, South Africa.
• Resuscitation. 2019 Apr 1; 137: 183-189.

AimThe design and implementation of a wearable training device to improve cardiopulmonary resuscitation (CPR) is presented.MethodsThe MYO contains both Electromyography (EMG) and Inertial Measurement Unit (IMU) sensors which are used to detect effective CPR, and the four common incorrect hand and arm positions viz. relaxed fingers; hands too low on the sternum; patient too close; or patient too far. The device determines the rate and depth of compressions calculated using a Fourier transform and dual-quaternions respectively. In addition, common positional mistakes are determined using classification algorithms (six machine learning algorithms are considered and tested). Feedback via Graphical User Interface (GUI) and audio is integrated.ResultsThe system is tested by performing CPR on a mannequin and comparing real-time results to theoretical values. Tests show that although the classification algorithm performed well in testing (98%), in real time, it had low accuracy for certain categories (60%), which are attributable to the MYO calibration, sampling rate and misclassification of similar hand positions. Combining these similar incorrect positions into more general categories significantly improves accuracy, and produces the same improved outcome of improved CPR. The rate and depth measures have a general accuracy of 97%.ConclusionThe system allows for portable, real-time feedback for use in training and in the field, and shows promise toward classifying and improving the administration of CPR.Copyright © 2019 Elsevier B.V. All rights reserved.

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