Wearable Inertial and Pressure Sensors-Based Chest Compression Quality Assessment to Improve Accuracy and Robustness

Chest compression (CC) quality is essential for saving patients' lives in cardiopulmonary resuscitation. This article presents a wearable CC quality assessment system integrating an inertial measurement unit (IMU) and a flexible pressure sensor. We proposed a CC depth (CCD) detection method that utilizes the spatiotemporal relationship between the pressure and depth to determine key timestamps and corrects the integration error by mapping the timestamps to velocity to exclude empty strokes. The method improves the CCD calculation accuracy of traditional acceleration dual integration algorithms. We also calculated the CC rate (CCR) based on the time points of the extremum of the pressure signal and evaluated the CC balance (CCB) by segmenting the pressure signal. Moreover, we used the residual pressure after CC release and the maximum pressure as observation indicators to evaluate the recoil adequacy adapting to different sternum rigidity. Furthermore, we conducted time- and frequency-domain analyses on the pitch angle signals and established a support vector machine (SVM)-based classifier to recognize incorrect arm and elbow postures. We performed CC experiments using the designed system and a manikin and constructed four datasets. We validated the proposed methods using the datasets for the multidimensional CC quality indicator evaluations. The absolute median errors of CCD and CCR detections were 1.17 mm and 0.11 times/min, respectively. The results indicated that our system and methods have high detection accuracy and robustness for future clinical applications.