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相关概念视频

Pulse rhythm01:30

Pulse rhythm

717
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
717

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An Application for Pairing with Wearable Devices to Monitor Personal Health Status
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使用智能手表手势识别监测阿片类药物使用障碍治疗遵守情况.

Andrew Smith1, Kuba Jerzmanowski1, Phyllis Raynor2

  • 1Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
概括
此摘要是机器生成的。

智能手表可以通过机器学习识别使用阿片类药物使用障碍 (OUD) 治疗的药物服用手势. 这项技术有望改善OUD管理中的药物坚持和患者监测.

关键词:
具有背景意识的环境生态瞬间评估 环境瞬间评估人类活动的认可 人类活动的认可机器学习是机器学习.药物检测 检测 药物检测神经网络的神经网络的神经网络智能医疗保健是一个智能医疗保健.可以穿戴的传感器.

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科学领域:

  • 数字健康数字健康
  • 医疗保健中的机器学习
  • 药物使用障碍治疗 药物使用障碍治疗

背景情况:

  • 阿片类药物流行病显著影响到阿片类药物使用障碍 (OUD) 的孕妇.
  • 有效监测药物坚持是OUD治疗成功的关键.
  • 目前对服用药物的监测方法可能是有限的.

研究的目的:

  • 探索使用机器学习算法与消费级智能手表的可行性.
  • 为了识别阿片类药物使用障碍 (OUD) 治疗的药物服用手势,特别是甲和布普伦诺芬.
  • 评估实时监测和改善药物坚持的潜力.

主要方法:

  • 使用Ticwatch E和E3智能手表,配有自定义的ASPIRE软件来收集手势数据.
  • 招募了16名女大学生,他们在一个受控的实验室环境中模拟服用药物的手势.
  • 采用RegNet风格的1D ResNet模型来分析智能手表收集的手势数据.

主要成果:

  • 机器学习模型在分类服用药物的手势方面取得了高性能.
  • 获得F1分数为0.89 (药物类型),0.88 (药物与日常活动) 和0.96 (任何药物手势).
  • 在区分不同的药物服用行为和日常活动方面证明了准确性.

结论:

  • 基于智能手表的手势识别是监测OUD药物坚持的一种可行的方法.
  • 这项技术有可能增强实时患者监测和改善治疗结果.
  • 由于模拟的手势和一个小的参与者池,需要进一步的现实世界的验证.