Abstract—Cardiopulmonary Resuscitation (CPR) is often trained using special manikins that provide feedback. When CPR is performed in out-of-hospital scenarios, the feedback can only come from sensors that are already on the rescuer, such as a smartwatch. This paper proposes and evaluates a method for detecting CPR quality using the sensors available in most smartwatch devices: accelerometers and gyroscope. We collect data of 18 nursing students performing CPR on a CPR manikin while wearing a wristband, and we used the manikin response to create a labeled dataset. Feature engineering includes extraction of vertical acceleration, Fourier analysis of acceleration data, and numerical integration to estimate push amplitude. This paper compares multiple machine learning models on top of the extracted features, with L1-regularized logistic regression producing the best results. The model achieved 90% of cross-validation accuracy and 80% of test set accuracy. Discussion of noise removal in the data provides the path for potential accuracy increase. The results of this work can contribute to the development of CPR feedback applications on smartwatches. This will provide a cheap and accessible solution to guide untrained people when CPR is needed.
 

Index Terms—cardiopulmonary resuscitation, CPR, machine learning, accelerometer, gyroscope, wristband, wearable sensors
 

Cite: Basel Kikhia, Andrey Boytsov, Alejandro Sanchez Guinea, and Andreas Prinz, "Utilizing a Wristband to Detect the Quality of a Performed CPR," Journal of Advances in Information Technology, Vol. 10, No. 4, pp. 123-130, November 2019. doi: 10.12720/jait.10.4.123-130