使用YOLOv8进行实时设备参数检测的自动健康监测系统
Mohammad Shafin Mahmood1, Mohammad Shoyaeb1, Aditta Chowdhury2
1Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology, Chattogram, Bangladesh.
Physical and engineering sciences in medicine
|November 17, 2025
概括
本研究介绍了一种使用YOLOv8和卷积神经网络的自动化医疗保健系统,通过生物医学设备监测患者的健康状况. 该系统在检测设备问题和识别生命体征方面实现了高精度,减少了手工劳动和提高了效率.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 对老年人和住院患者进行定期的健康监测是必不可少的,但通常受到医生的可用性和频繁的医院访问不切实际性的限制.
- 当前的医疗保健系统面临效率和人工劳动方面的挑战,需要自动化解决方案.
- 医疗保健系统与网络技术的整合为自动化监测过程提供了一条途径,而不会影响有效性.
研究的目的:
- 开发一种自动化系统,使用改进的生物医学设备监测患者的健康状况.
- 通过自动化提高医疗监测的效率并减少手工劳动.
- 验证拟议的自动化系统的准确性和实时功能.
主要方法:
- 利用光学字符识别 (OCR) 与YOLOv8用于更快地检测生物医学设备上的关注区域.
- 开发了两个不同的图像数据集用于训练YOLOv8模型.
- 采用一个卷积神经网络 (CNN) 模型来实时识别医疗设备的参数值,通过1000张图像进行验证.
主要成果:
- YOLOv8模型在检测医疗器械上的关键区域方面达到99.5%的准确性.
- 该CNN模型在实时识别各种医疗设备的参数值时显示了99.7%的准确性.
- 开发的系统成功地自动化了生物医学设备的监控.
结论:
- 建议的自动化医疗保健系统,利用YOLOv8和CNN,显著提高了患者健康监测的效率.
- 该系统在设备问题检测和价值识别方面的高精度为远程和医院患者护理提供了可靠的解决方案.
- 未来的工作可以扩展该系统,包括额外的医疗设备,如心率监测器和脉冲氧计.
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