使用混合CNN-GRU和MRF框架的联合室内本地化和活动识别的新方法
Sarmad Sohaib1, Syed Mohsin Bokhari2, Muhammad Shafi3
1Department of Electrical and Electronic Engineering, University of Jeddah, Jeddah, Saudi Arabia.
PloS one
|August 7, 2025
概括
这项研究介绍了一种混合模型,该模型结合了卷积神经网络门隔循环单元 (CNN-GRU) 和马尔科夫随机场 (MRF),用于智能家居中准确的室内定位和活动识别.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 无处不在的计算 无处不在的计算
背景情况:
- 准确的室内定位和活动识别对于智能环境至关重要.
- 现有的方法经常在杂和动态的室内条件下扎.
- 整合时空数据和活动依赖性是一项挑战.
研究的目的:
- 为联合室内定位和活动识别提出一种新的混合模型.
- 通过利用CNN-GRU和MRF来提高分类准确度.
- 在现实的智能家居场景中评估系统的性能.
主要方法:
- 这是一个混合模型,它结合了卷积神经网络门式反复单元 (CNN-GRU) 和马尔科夫随机场 (MRF).
- CNN-GRU捕获传感器数据中的空间和时间依赖性.
- 通过共同的概率分布,MRF模拟了活动和地点之间的相互关系.
主要成果:
- 在活动识别方面实现了95%的准确性.
- 在室内定位方面实现了93%的准确性.
- 在活动位置分类方面获得了81%的综合精度.
结论:
- 混合CNN-GRU-MRF系统在智能环境中展示了强大的预测能力.
- 该模型是高效的,可部署的,适用于医疗保健和智能生活应用.
- 有效地应对噪音和动态室内环境所带来的挑战.
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