使用机器学习,物联网和可穿戴传感器进行压力监测
Abdullah A Al-Atawi1, Saleh Alyahyan2, Mohammed Naif Alatawi3
1Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
这项研究介绍了Stress-Track,这是一种新的机器学习系统,使用可穿戴传感器通过体温,汗水和运动来监测压力水平. 它达到99.5%的准确性,为智能医疗保健中的压力管理提供了积极的方法.
科学领域:
- 医疗信息学 医疗信息学
- 可穿戴技术可穿戴技术
- 医疗保健中的机器学习
背景情况:
- 物联网 (IoT) 正在通过相互连接的监控设备改变医疗保健.
- 可穿戴式传感器和物联网使成本有效,持续的健康跟踪和压力监测成为可能.
- 心理压力显著影响生理健康,需要主动管理.
研究的目的:
- 引入一种基于机器学习的新系统,Stress-Track,用于主动监测压力水平.
- 利用可穿戴技术和物联网来改善健康和预防性健康管理.
- 在智能医疗保健中解决有效的压力管理解决方案的关键需求.
主要方法:
- 开发了一种名为Stress-Track的机器学习系统.
- 使用可穿戴传感器收集身体温度,汗水和运动速率的数据.
- 实施无线通信通道,用于实时数据传输和分析.
主要成果:
- 压力追踪系统在监测压力水平方面表现出99.5%的高准确率.
- 该系统有效地分析生理参数,以预测和跟踪压力.
- 成功地将机器学习与可穿戴物联网设备集成到健康应用中.
结论:
- 压力追踪为压力管理提供了一个有希望,准确和主动的解决方案.
- 该系统具有显著的潜力,可以增强预防性医疗保健和患者健康.
- 这项研究强调了物联网和机器学习在个性化健康监测中日益增长的作用.
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