基于深度学习的物联网系统,用于实时远程监控和早期检测健康问题
Md Reazul Islam1, Md Mohsin Kabir1, Muhammad Firoz Mridha2
1Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究介绍了一种物联网 (IoT) 系统,用于远程健康监测. 它使用传感器和人工智能来检测心脏病和发烧,在需要时将患者与医生联系起来.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 人口老龄化和慢性疾病需要先进的远程健康监测解决方案.
- 物联网 (IoT) 为有效的远程患者护理提供了一个有希望的技术框架.
- 目前的医疗保健系统面临的挑战是持续监测患者和早期发现疾病.
研究的目的:
- 提出和开发基于物联网的系统,用于实时远程健康监测和在家庭环境中早期检测健康问题.
- 整合多个生理传感器,以全面获取患者数据.
- 为了利用深度学习进行准确的疾病分类和患者-医生连接.
主要方法:
- 使用了MAX30100 (SpO2,心率),AD8232 (心电图) 和MLX90614 (体温) 传感器.
- 实现了MQTT协议,用于向中央服务器安全传输数据.
- 采用预先训练的深度学习模型 (CNN与注意) 来进行健康数据分析和分类.
主要成果:
- 该系统准确地分类了五种类型的心跳,并检测了发烧/无发烧情况.
- 提供心率和血液氧气水平的实时监测,并提供范围报告.
- 对于严重异常,自动连接到最近的医生可确保及时的医疗干预.
结论:
- 开发的物联网系统增强了用于慢性疾病管理的远程健康监测能力.
- 人工智能和多传感器数据的整合有助于早期检测和个性化患者护理.
- 这项技术有可能通过及时干预来改善患者的治疗结果,并降低医疗保健成本.
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