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相关实验视频

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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
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在智能手机按键动态中探索与年龄相关的模式,考虑时间变化:用基于人工智能的分析进行横截面研究.

Junhyung Moon1, Yu Lim Huh2, Hee Young Cho3,4

  • 1Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, Republic of Korea.

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概括

智能手机打字模式显示了与年龄相关的差异,年轻人打字速度更快,老年人打字速度更慢. 这些按键动态可以作为与年龄相关的功能变化的被动数字生物标志物.

关键词:
与年龄相关的行为.人工智能的人工智能是人工智能.数字生物标志物数字生物标志物按键的动态 按键的动态移动健康的移动健康手机电话 手机电话手机电话被动感应是一种被动感应.时间变化的时间变化.

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科学领域:

  • 数字生物标志物数字生物标志物
  • 人与计算机的互动.
  • 老年学是指老年学的学科.

背景情况:

  • 智能手机上的按键动态为被动数字生物标志物提供了一个有希望的途径.
  • 之前的研究已经探讨了各种疾病中键盘冲击的动态,但对一般人群中与年龄相关的变化知之甚少.

研究的目的:

  • 研究移动键盘按键动态中的与年龄相关的模式,重点关注每天的时间变化.
  • 为了识别不同年龄组特定的行为特征.
  • 评估人工智能 (AI) 模型在估计按键数据的年代年龄时的准确性.

主要方法:

  • 一项实地研究收集了韩国177名健康成年人的打字数据,他们使用自定义的Android键盘应用程序在几周内.
  • 43个行为特征 (速度,频率,可变性) 从按键时间和键类型中提取出来.
  • 包括LSTM和变压器在内的多个AI模型使用6小时,每日和每周的数据分辨率进行训练,并使用自定义损失函数来最大限度地降低预测变化.

主要成果:

  • 描述性分析显示,年轻的参与者比年长的参与者打字更快,更频繁,打字速度更慢,变化更大.
  • 长期短期记忆 (LSTM) 模型实现了最佳年龄估计 (平均绝对误差:3.69年,R2=0.71),改进到3.60 MAE,具有自定义丢失函数.
  • 功能重要性表明,早上和晚上打字模式对年龄有最大的歧视性.

结论:

  • 智能手机按键动态表现出对年龄敏感的行为模式,特别是在精细的时间分析中.
  • 这些动态表现出潜在的被动性,不显眼的标志物为与年龄相关的功能特征.
  • 这些发现可能支持未来的数字健康应用,用于对年龄敏感的个性化和与年龄相关的衰退的早期检测.