从脉冲波中直接提取呼吸信息,使用灵活的压力传感器系统
Xikuan Zhang1, Jin Chai2, Lingxiao Xu3
1Key Laboratory of Instrumentation Science and Dynamic Measurement Ministry of Education, North University of China, Taiyuan, 030051, China.
Microsystems & nanoengineering
|May 16, 2025
概括
一个新的可穿戴手腕传感器提供了舒适的长期呼吸监测. 该系统使用灵活的压力传感器和深度学习来准确跟踪医疗保健应用的呼吸模式.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 传感器技术 传感器技术
背景情况:
- 持续的呼吸系统监测对于呼吸系统疾病管理至关重要.
- 现有的设备往往是不舒服和侵入性的,限制长期使用.
- 需要用户友好,不引人注目的呼吸系统监测解决方案.
研究的目的:
- 开发一种灵活,轻量化,小型化的手腕式系统,用于长期的呼吸监测.
- 创建一个具有成本效益和耐用性的传感器,用于持续收集生理数据.
- 利用深度学习来准确地分类呼吸系统状态.
主要方法:
- 通过丝网打印制造一个高灵敏度的灵活压力传感器,经过耐用性测试 (>70,000循环).
- 集成信号采集电路和用于数据收集的移动客户端.
- 开发一个双向长期短期记忆 (BiLSTM) 神经网络,其中包含一个用于呼吸状态分类的残余模块.
主要成果:
- 灵活的压力传感器显示出高灵敏度 (11,847.24 kPa−1).
- BiLSTM神经网络在分类各种呼吸状态 (缓慢,正常,快速,模拟呼吸) 中达到99.5%以上的准确性.
- 该系统被证明是稳定的,具有成本效益和耐用性,用于定量呼吸系统数据收集.
结论:
- 一个新的,可穿戴的呼吸监测系统已经成功开发出来.
- 该系统为长期呼吸状况跟踪提供了舒适有效的解决方案.
- 这项技术在个人健康监测和临床应用中具有广泛应用的巨大潜力.
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