迈向可穿戴呼吸监测:1D-CRNN基于智能织品中的呼吸检测
Tobias Steinmetzer1, Sven Michel1
1Department of Therapy Sciences (II), Brandenburg University of Technology (BTU) Cottbus-Senftenberg, Platz der Deutschen Einheit 1, 03013 Cottbus, Germany.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
一个新的一维卷积循环神经网络 (1D-CRNN) 从智能电子织数据准确地分类呼吸活动. 这种可穿戴系统提供近乎实时的生命体征监测,具有高精度和效率.
科学领域:
- 可穿戴技术是可穿戴的技术.
- 生物医学工程 生物医学工程
- 机器学习用于医疗保健
背景情况:
- 呼吸活动监测对于生理健康评估至关重要.
- 智能电子织品提供不引人注目的生命体征监测功能.
- 精确的呼吸模式分类对于可穿戴健康系统至关重要.
研究的目的:
- 开发和评估1D-CRNN用于自动呼吸活动分类.
- 通过智能电子织品的惯性数据来评估1D-CRNN的性能.
- 为了证明拟议的方法对可穿戴应用的近实时可行性.
主要方法:
- 使用1D-CRNN集成卷积和递归层用于特征提取和时间建模.
- 训练并评估了59名受试者的惯性数据模型,使用分层五倍交叉验证.
- 评估不同窗口大小的性能,重点关注准确性和F1分数.
主要成果:
- 在2000个样本窗口大小中,获得了0.88的平均精度和0.92的F1得分.
- 最好的单折配置达到了0.995的精度和0.99.9的F1得分.
- 演示了接近实时的处理 (1.76s为250s测量),比记录时间快100倍以上.
结论:
- 1D-CRNN有效地根据电子织物惯性数据对呼吸活动进行分类.
- 提出的方法适用于嵌入式,在可穿戴系统中的设备上的推断.
- 高精度和实时性能可用于持续健康监测的实际应用.
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