Using an Arduino Board and a Piezoelectric Hand-Worn Sensor to Display Pressure and Depth for Cardiac CPR: An Artificial Intelligence-Based Approach in Training Physicians and Nurses

High-quality cardiopulmonary resuscitation (CPR) requires the application of precise pressure and depth tailored to the patient's age. Nevertheless, human errors during both training and real-life CPR delivery are prevalent. The aim of this study was to design, construct, and evaluate a training hand-worn device equipped with an Arduino board and a piezoelectric sensor that measures the applied force and chest compression depth in real time, displaying the values on a user interface. The core innovation of this system lies in integrating an artificial intelligence (AI) module—a multilayer perceptron neural network—to analyse sensor data and provide personalized feedback based on the patient's age group (adult, child, infant). By continuously comparing instantaneous pressure and depth with evidence-based optimal ranges, the system generates audio-visual guidance and, upon session completion, quantitatively assesses the trainee's performance. Findings from a quasi-experimental study involving ۴۰ nursing students demonstrated that using this smart hand-worn device increased the percentage of compressions with correct depth by ۳۸% and correct pressure by ۴۳% compared to conventional training (p < ۰.۰۱). The results indicate a considerable effectiveness of integrating wearable sensor technology and artificial intelligence in teaching critical medical skills.

کلیدواژه ها:

Artificial Intelligence ، Cardiopulmonary Resuscitation Training ، Piezoelectric Sensor ، Arduino ، Medical Education System