Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments

TL;DR

This paper develops a comprehensive framework for personalized indoor localization in multi-floor smart environments, integrating probabilistic modeling, machine learning, and boosting algorithms to enhance accuracy and context-awareness for diverse users.

Contribution

It introduces a novel interdisciplinary approach combining probabilistic reasoning, machine learning, and boosting techniques for personalized indoor localization in multi-user, multi-floor settings.

Findings

Achieved higher localization accuracy for individual users compared to average user models.

Successfully detected user floor-specific locations and inside/outside status.

Validated on real-world data from 18 users across 3 buildings with 5 floors.

Abstract

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.