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基于包含预设注意力的动态关键点的跌倒检测.

Kun Zheng¹, Bin Li¹, Yu Li¹

¹Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.

Mathematical biosciences and engineering : MBE

|June 16, 2023

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此摘要是机器生成的。

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使用条款与条件

在老年人中,准确的跌倒检测至关重要. 这项研究引入了一种新的深度学习模型,该模型融合了人体姿势和动态关键点信息,显著提高了跌倒检测准确度,以改善老年人护理.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
老年学是一门学科.

背景情况:

意外跌倒是老年人群的主要健康风险.
目前基于视频的深度学习落检测方法通常仅依赖于人体姿势或关键点.
需要更准确,更强大的落检测系统来减轻落的影响.

研究的目的:

提出一种新的跌倒检测算法,它结合了人类姿势和动态关键点信息.
通过使用监控视频,提高老年人落检测的准确性.
通过更可靠的跌倒检测技术,改善老年人护理.

主要方法:

开发了一个预设的注意力捕捉机制,用于将图像输入到培训网络中.
引入了"动态关键点"的概念,以解决跌倒时不完整的姿势信息.
融合了动态关键点信息与原始的人体姿势图像,并采用了注意力预期机制.

主要成果:

拟议的模型有效地将人类动态关键点信息与姿势图像融合在一起.
在跌落检测数据集和上下跌检测数据集上的实验显示,跌落检测的准确性有所提高.
动态关键点模型纠正了原始人体姿势图像模型所造成的检测错误.

关键词:

补充性纠正补充性纠正合并决定合并决定动态关键点动态关键点落检测系统落检测系统落检测系统预先设定的注意力.

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结论:

拟议的摔倒检测算法通过整合补充数据源显著提高了准确性.
这种方法为检测老年人群中跌倒提供了更强大的解决方案.
开发的模型为老年护理应用提供了更好的支持.