Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

TL;DR

This paper introduces multimodal indoor localization methods for smart homes, utilizing BLE data and sensor signals, achieving over 81% accuracy, and compares various machine learning models for optimal performance.

Contribution

It presents novel Big-Data driven and context-independent approaches for indoor localization in IoT environments, outperforming existing methods in accuracy and error metrics.

Findings

Achieved 81.36% and 81.13% accuracy with two proposed methods.

Outperformed existing localization techniques based on root mean squared error.

Compared multiple machine learning models to identify the best approach.

Abstract

This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user's indoor location in a specific activity-based zone during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the zone-based indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user's indoor position that outperforms all similar works in this field, as per the associated root mean squared error - one of the performance evaluation metrics in ISO/IEC18305:2016- an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization.