# Feedback Devices for Cardiopulmonary Resuscitation: A Narrative Review
## 2 Classification of Feedback Devices for Cardiopulmonary Resuscitation
### Research on Feedback Devices Based on Physiological Information Data Monitoring in China and Abroad  
Simple feedback systems have been shown to improve the effectiveness of chest compression during CPR. However, a limitation of such systems is the lack of personalization for individual patients and a standardized execution. To address this issue, feedback devices utilizing physiological data monitoring are needed. Due to the limitations of the prehospital environment, invasive detection is often difficult. Therefore, non-invasive detection of physiological parameters of cardiac arrest patients is crucial in the development of a closed-loop feedback module for automatic chest compressors. By monitoring real-time physiological parameters and observing the effects of chest compression, intelligent closed-loop control can be achieved.  
A study by [PERSON] et al. at Northwell Health established a time-dependent function correlating carotid blood flow during cardiac arrest resuscitation and response to varying chest compression. By predicting the carotid blood flow generated by subsequent chest compression, the study aimed to optimize CPR parameters in real time, maximizing carotid blood flow [29]. [PERSON] et al. evaluated the feasibility of using a machine-controlled closed-loop CPR (MC-CPR) system to optimize coronary perfusion pressure (CPP) during CPR. Their real-time hemodynamic simulations demonstrated that MC-CPR, controlled by a closed-loop machine, significantly outperformed the AHA CPR guidelines in improving coronary perfusion pressure [30]. [PERSON] et al. proposed a machine learning algorithm for impedance-based ventilation detection in mechanical CPR. Using adaptive signal processing, they demonstrated feasible ventilation detection and suggested ventilation rate feedback during resuscitation [31].  
In China, researchers at the Academy of Military Medical Sciences, including [PERSON] et al., have developed a mechanical chest compression closed-loop control system. The system consists of two parts: the LabVIEW control platform and the mechanical chest compression controller. A fuzzy neural network and fuzzy PID control strategy are used to effectively regulate compression depth. The closed-loop control algorithm combines beneficial factor (BF), which reflects blood flow perfusion degree, and dangerous factor (RF), which reflects rib fracture risk, based on chest compression resuscitation theory and intelligent control theory. The algorithm-enhanced chest compression system, verified through a LUCAS automatic chest compressor, outperforms traditional systems [32]. In another study, [PERSON] et al. proposed a chest compression closed-loop controller based on a fuzzy control algorithm that adjusts the compression depth according to CPP and evaluated the performance of the fuzzy controller by computer simulation and compared it with the traditional PID controller [33]. In 2021, [PERSON] et al. from Shandong University developed an auxiliary control system for a chest compression device based on ETCO\({}_{2}\). The system seamlessly integrates ETCO\({}_{2}\) measurement with an automated chest compression device, enabling precise tracking of compression depth and frequency. By determining the difference signal, it dynamically adjusts the compression depth and frequency of the chest compression device based on the determined control signal [34].