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深度功能融合与计算机视觉驱动的摔倒检测方法,以提高辅助生活安全性
Wafa Sulaiman Almukadi1, Fadwa Alrowais2, Muhammad Kashif Saeed3
1Department of Software Engineering, College of Engineering and Computer Science, University of Jeddah, Jeddah, Saudi Arabia.
Scientific reports
|September 15, 2024
概括
这项研究介绍了一种新的深度特征融合与计算机视觉用于降落检测和分类 (DFFCV-FDC) 技术. DFFCV-FDC方法通过使用深度学习进行准确的实时摔倒检测,提高了老年人在辅助生活中的安全性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 老年学是一门学科.
背景情况:
- 辅助生活设施需要对老年人采取强有力的安全措施,由于高发病率和受伤风险,跌倒检测至关重要.
- 传统的跌倒检测方法有局限性;整合计算机视觉 (CV) 和深度学习 (DL) 提供了一种革命性的方法,用于持续监测和实时事件识别.
研究的目的:
- 引入和评估一种新技术,即深度功能融合与计算机视觉的摔倒检测和分类 (DFFCV-FDC),用于在辅助生活中增强老年人的摔倒检测.
- 利用CV和DL精确地实时识别落事件,最大限度地减少错误报警,并使主动干预成为可能.
主要方法:
- DFFCV-FDC技术使用高斯过 (GF) 来减少视觉数据中的噪声.
- 一个深度功能融合过程集成了MobileNet,DenseNet和ResNet模型,用于全面的功能提取.
- 超参数优化是使用改进的优化算法 (IPOA) 进行的,而跌倒检测是通过无音自编码器 (DAE) 模型完成的.
主要成果:
- DFFCV-FDC方法论在跌倒检测和在基准跌倒数据库上的分类方面表现出卓越的表现.
- 对比分析证实了DFFCV-FDC方法优于现有的落检测技术的优势.
结论:
- DFFCV-FDC技术显著提高了在辅助生活环境中的落检测系统的效率和可靠性.
- 这种CV和DL驱动的方法可以及时进行干预,改善老年居民的安全和福祉.
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