Room Recognition Using Discriminative Ensemble Learning with Hidden Markov Models for Smartphones

TL;DR

This paper presents a novel ensemble learning approach combining Hidden Markov Models with discriminative methods for accurate room-level localization on smartphones in indoor environments.

Contribution

It introduces an efficient ensemble learning method that improves indoor localization accuracy using smartphones and Wi-Fi, outperforming traditional approaches.

Findings

Achieves high room-level localization accuracy

Overcomes traditional machine learning methods

Effective in office-like indoor environments

Abstract

An accurate room localization system is a powerful tool for providing location-based services. Considering that people spend most of their time indoors, indoor localization systems are becoming increasingly important in designing smart environments. In this work, we propose an efficient ensemble learning method to provide room level localization in smart buildings. Our proposed localization method achieves high room-level localization accuracy by combining Hidden Markov Models with simple discriminative learning methods. The localization algorithms are designed for a terminal-based system, which consists of commercial smartphones and Wi-Fi access points. We conduct experimental studies to evaluate our system in an office-like indoor environment. Experiment results show that our system can overcome traditional individual machine learning and ensemble learning approaches.