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相关概念视频

Equipments Used to Measure Body Temperature01:13

Equipments Used to Measure Body Temperature

Body temperature can be assessed using various devices and measured in Celsius or Fahrenheit.
Glass-bulb Thermometer:
Glass-bulb thermometers are hollow glass tubes with a bulb tip containing liquid such as ethanol or mercury. Historically, glass bulb mercury thermometers were the standard device to measure body temperature. Today, mercury thermometers are prohibited in many countries due to the hazardous effects of mercury and the risk of exposure if the glass bulb breaks. In general,...
Assessing Body Temperature - Axilla01:14

Assessing Body Temperature - Axilla

Procedural Guide for Assessing Axillary Body Temperature using a Digital Thermometer:
Step 1: Perform hand hygiene and put on clean gloves to maintain infection control and prevent cross-contamination.
Step 2: Prepare the patient by explaining the procedure to ensure understanding and cooperation. Ensure privacy, expose the axilla, and inform the patient that minimal movement is crucial for an accurate reading.
Step 3: Adjust the patient’s clothing to expose only the axilla. It minimizes...

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相关实验视频

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基于惯性测量单位的冷肩部识别从每日肩部任务使用机器学习方法.

Chien-Pin Liu1, Ting-Yang Lu2, Hsuan-Chih Wang1

  • 1Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei City 112, Taiwan.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
概括

本研究引入了一种基于惯性测量单位 (IMU) 的系统,用于客观地评估冷肩部 (FS). 机器学习准确地识别FS患者的肩膀任务,提供一个可行的临床工具.

关键词:
结的肩膀 结的肩膀识别系统的识别系统惯性测量单位是一种惯性测量单位.机器学习是机器学习.

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科学领域:

  • 生物医学工程 生物医学工程
  • 康复技术 康复技术 康复技术
  • 医疗保健中的机器学习

背景情况:

  • 冷的肩膀 (FS) 导致肩膀疼痛和有限的运动范围 (ROM),目前的评估是主观的.
  • 需要客观的评估方法来提高FS诊断和监测的准确性.
  • 惯性测量单元 (IMU) 为客观运动分析提供了一个潜在的解决方案.

研究的目的:

  • 开发和评估一个基于IMU的系统,用于自动识别FS和没有FS的个体的肩负任务.
  • 为了比较不同的机器学习 (ML) 和深度学习 (DL) 模型在FS识别方面的有效性.
  • 分析特征类型和传感器放置对识别性能的影响.

主要方法:

  • 利用两个IMU连接到24名FS患者和20名健康受试者的手臂和手腕,每天执行五项肩部任务.
  • 提取并分析了两个特征组:时间域统计特征和动态特征.
  • 应用了七种ML技术和两种DL模型 (卷积神经网络,多层感知器) 来确定任务.

主要成果:

  • 基于DL的系统实现了最高的识别性能,CNN达到88.26%的准确性,MLP达到89.23%的F1得分.
  • 基于手腕的IMU功能比基于手臂的功能提供了更高的准确性.
  • 与动力学特征相比,时间域统计特征在FS识别方面表现出更好的区分能力.

结论:

  • 基于IMU的系统使用ML是可行的客观FS评估在临床实践中.
  • 深度学习模型,特别是CNN,显示出准确的FS任务识别的巨大潜力.
  • 优化传感器放置 (手腕) 和功能选择 (时间域) 可以提高系统性能.