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相关概念视频

Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short distances...

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相关实验视频

Updated: Jun 23, 2026

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
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通过内置设备传感器在6至11岁儿童中推进客观移动设备使用测量:一项概念验证研究

Olivia L Finnegan1, R Glenn Weaver1, Hongpeng Yang2

  • 1Department of Exercise Science, University of South Carolina, Columbia, South Carolina, USA.

Human behavior and emerging technologies
|December 19, 2025
PubMed
概括

机器学习模型通过传感器数据准确地识别了使用iPad的个体儿童,改善了屏幕时间研究. 这项技术显示出对客观跟踪儿童设备使用的承诺,而无需自我报告.

关键词:
使用数字媒体使用数字媒体.移动屏幕使用 移动屏幕使用这是客观的测量.

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科学领域:

  • 数字媒体和儿童发展
  • 人与计算机的互动.
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 移动设备融入儿童生活是普遍存在的.
  • 目前的屏幕时间测量方法 (自我报告,被动传感) 在准确性和用户识别方面存在局限性.
  • 行为生物识别身份验证为客观用户识别提供了一个潜在的解决方案.

研究的目的:

  • 通过使用传感器数据,评估机器学习模型在识别iPad独特儿童用户方面的初步准确性.
  • 探索在屏幕时间研究中使用移动设备传感器进行持续用户身份验证的可行性.

主要方法:

  • 收集了9名儿童 (6-11岁) 的iPad传感器数据 (加速计,陀螺仪,磁力计).
  • 开发和训练了5个机器学习模型 (逻辑回归,支持向量机,神经网络,k-最近邻居,随机森林),使用57个特征.
  • 使用F1评分,准确度,精度和回忆来评估模型性能,与80%-20%的火车测试分割.

主要成果:

  • 机器学习模型在识别独特的iPad用户方面表现出很高的表现.
  • 随机森林和k-最近邻居模型获得了最高的F1得分 (0.94).
  • 所有车型的F1分数从0.75到0.94.9不等.

结论:

  • 现有的移动设备传感器可以有效地用于持续的用户识别.
  • 这种方法具有显著的潜力,可以提高儿童屏幕时间测量的准确性.
  • 需要在更大的样本和现实环境中进行进一步的研究.