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相关概念视频

Introduction to Stress and Lifestyle01:27

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Stress is a multifaceted response to events perceived as challenging or threatening, highlighting physical, emotional, cognitive, and behavioral reactions. Physically, stress can lead to fatigue, sleep disruptions, and various health issues such as frequent colds, chest pains, and nausea. Emotionally, it can manifest as anxiety, depression, irritability, and anger triggered by both minor and major life events. Cognitively, it may result in difficulty in concentration, memory, and...
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个性化压力移动传感使用自我监督的预训练.

Tanvir Islam1, Peter Washington1

  • 1Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI 96822, USA.

Applied sciences (Basel, Switzerland)
|November 7, 2024
PubMed
概括
此摘要是机器生成的。

使用自主监督学习 (SSL) 的个性化压力预测模型显著减少了对标记生物信号数据的需求. 这一进步使得数字健康干预的更准确,实时的压力检测成为可能.

关键词:
有影响力的计算.生物信号 生物信号移动传感传感器是移动传感器.个性化的机器学习自主监督学习学习压力预测 压力预测

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科学领域:

  • 移动传感和可穿戴技术
  • 机器学习用于健康.
  • 数字健康和个性化医疗

背景情况:

  • 压力是一个主要的健康问题,通过可穿戴设备实时预测可以实现及时干预.
  • 电皮活动 (EDA) 是压力的常见生物信号,但机器学习预测面临主观标签和稀疏数据等挑战.
  • 模型个性化,为每个用户培训单独的模型,是解决这些挑战的有希望的方法.

研究的目的:

  • 调查自主监督学习 (SSL) 对于预训神经网络进行个性化压力预测的有效性.
  • 通过个性化模型培训,通过最小的用户标记数据实现准确的压力检测.
  • 提高移动传感的可行性,以应对压力等复杂和主观的健康结果.

主要方法:

  • 在生物信号数据上使用一维卷积神经网络 (1D CNN) 进行预训练,并使用自我监督学习 (SSL) 进行自我监督学习.
  • 雇佣模型个性化,为每个用户培训个性化压力预测模型.
  • 评估了可穿戴式压力和影响检测 (WESAD) 数据集的方法,将SSL预训练模型与监督基线进行比较.

主要成果:

  • 通过SSL预训练,个性化模型能够超过监督的基线.
  • 与非个性化的SSL模型相比,使用SSL训练的模型需要少于30%的标记数据才能达到同等的性能.
  • 证明了SSL学习嵌入的有效性,用于个性化压力预测.

结论:

  • 使用SSL预训练进行个性化压力预测是有效的,数据效率高.
  • 这种方法促进了精确的卫生系统,需要最小的用户注释.
  • 能够实现先进的移动传感,用于主观和异质的健康结果,如压力.