一个基于深度学习架构的新型框架,用于用惯性传感器连续识别人类活动.
Vladimiro Suglia1, Lucia Palazzo1,2, Vitoantonio Bevilacqua1,3
1Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, 70126 Bari, Italy.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
本研究引入了用于持续人类活动识别 (CHAR) 的深度学习框架. 该模型准确地实时分类运动动作,在临床环境中提供远程患者监测的潜力.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 人类活动识别 (HAR) 框架对于在临床环境中监测患者的运动和功能能力非常有价值.
- 深度学习 (DL) 模型可以处理HAR的原始数据,减少对手动功能工程的需求.
- 现有的基于DL的HAR研究往往缺乏对持续执行的行动的关注,突出显示了现实世界的适用性上的差距.
研究的目的:
- 设计,开发和测试基于深度学习 (DL) 的工作流程,用于持续的人类活动识别 (CHAR).
- 为了解决当前关于正在进行的运动动作HAR框架的文献中的局限性.
- 评估拟议的CHAR框架的潜在临床实用性.
主要方法:
- 专门为CHAR.开发基于DL的工作流程.
- 训练模型的数据来自十个健康的受试者.
- 在八个不同主题上测试框架的性能,以评估概括性.
主要成果:
- 拟议的DL框架证明了准确的运动动作分类的能力.
- 该系统在可行的时间框架内实现了分类,表明实时潜力.
- 在与培训套件不同的主题上的成功测试表明了概括性.
结论:
- 为CHAR开发的DL工作流能够及时准确分类运动动作.
- 该框架有望在临床场景中应用,特别是在远程患者监测方面.
- 用更大的数据集进行进一步验证是有必要的,但初步结果对持续活动识别是令人鼓舞的.
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