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.

作者提出了一种多功能跨学科框架，用于基于概率推理的数学方法对个体用户进行建模，机器学习对用户特定的用户交互进行建模，以及引入了渐进提升和 AdaBoost 算法等多个算法以实现室内定位。该框架引入了两种新功能，可为室内定位提供语义上下文，并在收集了 254 个室内区域的五层楼内的 18 个不同用户的大量数据集的基础上进行了测试。实验结果表明该模型的室内定位始终比传统模型更准确。