论文标题
在多层智能环境中，室内定位，用于个性化的环境协助多个用户的生活
Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments
论文作者
论文摘要
本文提出了一个多功能的跨学科框架，为个性化的环境辅助生活做出了四项科学贡献，其特定重点是满足智能生活环境未来各种老化人群的不同和动态需求。首先，它提出了一种基于概率推理的数学方法，以对这些环境中多个用户的用户多样性产生的任何活动建模所有可能的用户交互形式。其次，它提出了一个使用这种方法与机器学习方法使用此方法的系统，以建模单个用户配置文件和特定用户的用户交互，以检测每个特定用户的动态室内位置。第三，为了满足建立高度准确的室内定位系统，以增加信任，依赖和无缝的用户接受，该框架引入了一种新颖的方法，其中将两种增强方法梯度提升和Adaboost算法集成并用于基于决策树的学习模型以执行室内定位。第四，该框架引入了两个新型功能，以在检测每个用户的特定地点的位置以及跟踪特定用户是否位于基于多层的室内设置的给定空间区域内或外部，以提供室内定位的语义上下文。这些新的框架的新功能是在与本地化相关的大数据数据集中测试的，这些数据集从18个不同的用户收集的数据集中，这些用户在3个建筑物中导航，该建筑物由5层和254个室内空间区域组成。结果表明，与对普通用户建模的传统方法相比，对每个特定用户建模的个性化AAL的室内定位方法始终达到更高的准确性。
This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living, with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living environments. First, it presents a probabilistic reasoning-based mathematical approach to model all possible forms of user interactions for any activity arising from the user diversity of multiple users in such environments. Second, it presents a system that uses this approach with a machine learning method to model individual user profiles and user-specific user interactions for detecting the dynamic indoor location of each specific user. Third, to address the need to develop highly accurate indoor localization systems for increased trust, reliance, and seamless user acceptance, the framework introduces a novel methodology where two boosting approaches Gradient Boosting and the AdaBoost algorithm are integrated and used on a decision tree-based learning model to perform indoor localization. Fourth, the framework introduces two novel functionalities to provide semantic context to indoor localization in terms of detecting each user's floor-specific location as well as tracking whether a specific user was located inside or outside a given spatial region in a multi-floor-based indoor setting. These novel functionalities of the proposed framework were tested on a dataset of localization-related Big Data collected from 18 different users who navigated in 3 buildings consisting of 5 floors and 254 indoor spatial regions. The results show that this approach of indoor localization for personalized AAL that models each specific user always achieves higher accuracy as compared to the traditional approach of modeling an average user.
