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相关概念视频

Assessing Body Temperature - Axilla01:14

Assessing Body Temperature - Axilla

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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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在使用非接触式技术的肩部绑架练习中估计关节角度.

Ali Barzegar Khanghah1,2, Geoff Fernie3,4,5, Atena Roshan Fekr3,4

  • 1KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, 550 University Ave, Toronto, M5G 2A2, ON, Canada. ali.barzegarkhanghah@mail.utoronto.ca.

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概括

这项研究引入了一种新的无标记器远程康复系统,使用LiDAR和骨跟踪进行精确的3D关节角度分析. 基于深度的方法增强了远程运动评估,提高了远程康复能力.

关键词:
校准 校准 校准 校准 校准 校准 校准没有标记器的关节角度估计.动作捕捉 运动捕捉系统验证系统验证远程康复疗法 远程康复疗法

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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
  • 计算机视觉 计算机视觉

背景情况:

  • 远程康复 (tele-rehab) 利用通信技术进行远程医疗保健,在COVID-19大流行期间变得越来越重要.
  • 现有的远程康复系统往往缺乏全面的运动分析能力.
  • 建议采用一种新的方法,将深度传感和骨跟踪集成在一起,以解决这一局限性.

研究的目的:

  • 开发和验证一个没有标记器的系统,用于在远程康复中精确的3D关节角度评估.
  • 为了评估 LiDAR 深度技术与骨架跟踪算法 (Cubemos,Mediapipe) 结合的性能.
  • 通过个性化的校准技术,提高关节角度计算的准确性.

主要方法:

  • 使用LiDAR摄像头收集深度视频和通过Motion Capture (Mocap) 系统收集的运动数据,来自14名参与者进行肩部绑架练习.
  • 集成LiDAR数据与Cubemos和Mediapipe骨架跟踪框架,以估计3D关节角度.
  • 通过将估计的关节角度与Mocap地面真相进行比较来验证系统,并应用各种回归模型进行校准.

主要成果:

  • 与Mediapipe相比,Cubemos框架在联合角度估计方面表现出更高的准确性.
  • 拟议的系统显示与Mocap有很强的相关性,尽管由于噪声造成的微小偏差.
  • 系统精度随着距离LiDAR传感器的距离增加而下降,但校准显著提高了精度,线性回归模型表现最好.

结论:

  • 这种基于深度的系统有效地跟踪身体关节和上肢角度,以实现远程康复.
  • 该系统与Mocap取得了强大的相关性,表明其具有精确联合跟踪的潜力.
  • 激光雷达的深度传感器使得精确的角度计算超出了传统RGB摄像机的范围,增强了远程康复应用.