利用联合学习和边缘计算来实现抗疫医疗保健
Atlanta Choudhury1, Kandarpa Kumar Sarma1, Vyacheslav Gulvanskii2
1Department of Electronics and Communication Engineering, Gauhati University, Guwahati, 781014, Assam, India.
Scientific reports
|July 2, 2025
概括
这项研究引入了一个新的AI系统,使用联合学习 (FL) 和物联网 (IoT) 来监控公共卫生协议. 该系统有效地检测口罩使用,社交距离和联系人追踪,提高了流行病的安全性.
科学领域:
- 计算机科学 计算机科学
- 公共卫生 公共卫生
- 人工智能的人工智能
背景情况:
- 后疫情时代需要先进的医疗基础设施和损害控制,技术发挥着至关重要的作用.
- 人工智能 (AI) 和物联网 (IoT) 对于开发响应式医疗系统至关重要.
- 联合学习 (FL) 对于增强人工智能决策支持而保持分散式学习至关重要.
研究的目的:
- 设计和评估一种符合流行病的机制,用于监测传染病协议和追踪接触者.
- 整合边缘计算,FL框架和物联网传感器,用于实时公共卫生监测.
- 在FL框架内评估YOLOv4和SENet注意层组合的新性能.
主要方法:
- 这项研究采用了联合学习 (FL) 框架,并使用边缘计算,使用Grove AI-Raspberry Pi 4块作为节点.
- 开发了YOLOv4和SENet注意层的组合,并与预先训练的深度学习模型 (RESNET-50,MobileNetV2,SocialdistancingNet-19) 进行了基准测试.
- 该系统被配置为监控社交距离,面罩使用,联系人追踪和住宅环境中的网络攻击.
主要成果:
- 在FL框架中的YOLOv4和SENet注意层组合在各种任务中表现出高精度.
- 具体的性能指标包括:面罩检测 (94.6%),不正确的面罩检测 (98%),面罩分类 (95.4%),社交距离监测 (96.1%),联系人追踪 (95.2%) 和网络攻击检测 (94.2%).
- 综合系统被证明可靠监测正确的面罩佩戴,社会距离和联系人追踪协议.
结论:
- 拟议的YOLOv4和SENet关注层与FL和IoT集成,为流行病协议监控提供了强大的解决方案.
- 这项技术对于维持生物安全规范和加强在传染病爆发时的公共卫生监测至关重要.
- 该系统在检测各种安全违规行为的有效性突出显示了其未来公共卫生应用的潜力.
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