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高质量心肺复苏智能教学与评估系统的实施

Implementation of a Smart Teaching and Assessment System for High-Quality Cardiopulmonary Resuscitation.

作者信息

Huang Li-Wen, Chan Yu-Wei, Tsan Yu-Tse, Zhang Qi-Xiang, Chan Wei-Chang, Yang Han-Hsuan

机构信息

Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan.

Department of Computer Science and Information Management, Providence University, Taichung 40301, Taiwan.

出版信息

Diagnostics (Basel). 2024 May 10;14(10):995. doi: 10.3390/diagnostics14100995.

Abstract

The purpose of this study is to develop a smart training and assessment system called , for teaching and training cardiopulmonary resuscitation (CPR), based on human posture estimation techniques. In this system, trainees can automatically recognize and evaluate whether chest compressions during CPR meet the standard of high-quality CPR by simply using a device such as a smart phone. Through the system, trainees are able to obtain real-time feedback on the quality of compressions so that they can adjust the cycle, depth, frequency, and posture of compressions to meet the standard of high-quality CPR. In addition, the SmartCPR system is convenient for CPR trainers. Trainers can instantly and accurately assess whether the trainee's compressions meet the standard of high-quality CPR, which reduces the risk of manual assessment errors and also reduces the trainer's teaching pressures. Therefore, the SmartCPR system developed in this study can be an important tool for CPR teaching and training for physicians, which can provide training and guidance for high-quality CPR maneuvers and enable trainees to become more proficient in CPR and self-training.

摘要

本研究的目的是基于人体姿态估计技术开发一种名为SmartCPR的智能培训与评估系统,用于心肺复苏术(CPR)的教学与培训。在该系统中,学员只需使用智能手机等设备,就能自动识别并评估CPR过程中的胸外按压是否符合高质量CPR的标准。通过该系统,学员能够获得关于按压质量的实时反馈,从而调整按压的周期、深度、频率和姿势,以达到高质量CPR的标准。此外,SmartCPR系统对CPR培训师来说也很方便。培训师可以即时、准确地评估学员的按压是否符合高质量CPR的标准,这降低了人工评估错误的风险,也减轻了培训师的教学压力。因此,本研究开发的SmartCPR系统可以成为医生CPR教学与培训的重要工具,可为高质量CPR操作提供培训和指导,使学员在CPR及自我训练方面更加熟练。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a7f/11120156/b0aca022aa44/diagnostics-14-00995-g001.jpg

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