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在可穿戴式传感器中取得十年的进展 (2015-2024):基于网络的可视化审查

Yifei Li^1,2, Pei Liu¹, Yan Fang³

¹Hikvision Research Institute, Hangzhou 310051, China.

Sensors (Basel, Switzerland)

|April 12, 2025

概括

用于跌倒检测的可穿戴传感器正在迅速发展,提高老年人的安全性. 研究表明,在这个领域有显著的增长,利用机器学习来准确检测落和减少伤害.

科学领域:

生物医学工程生物医学工程
老年学是一门学科.
计算机科学计算机科学

背景情况:

可穿戴式传感器对于老年人的安全和减少跌倒相关伤害至关重要.
显著的研究重点集中在开发有效的摔倒检测系统上.
现有系统在现实世界的部署和准确性方面面临挑战.

研究的目的:

分析可穿戴式摔倒检测研究趋势,关键技术和合作.
审查用于落检测的数据集和机器学习技术.
提供全面的进展和未来方向的概述.

主要方法:

使用CiteSpace.Space进行基于网络的可视化分析.
从SCI和SSCI指数期刊 (2015-2024) 中分析了582篇文章和65篇评论.
审查各种数据集和机器学习算法,包括深度学习.

主要成果:

关于可穿戴式落检测传感器的研究出版物显著增加.
机器学习和深度学习模型在受控环境中表现出高精度,F1分数,灵敏度和特异性.
确定关键技术和协作研究网络.

结论:

可穿戴式摔倒检测技术是一个快速发展的领域,研究的增长很大.
先进的机器学习技术显示出准确的跌落检测的前景.
这一审查为可穿戴式落检测系统的未来进步提供了基础.

关键词:

物联网的物联网,就是物联网.落检测系统落检测系统落检测系统防摔防止跌倒预防跌倒惯性传感器是一种无动态传感器.在撞击前的跌倒.可穿戴式传感器传感器

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