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

• URL: http://arxiv.org/abs/2106.15606v1
• Date: Tue, 29 Jun 2021 17:46:21 GMT
• Title: Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes
• Authors: Nirmalya Thakur and Chia Y. Han
• Abstract summary: It presents a Big-Data driven methodology that studies the multimodal components of user interactions. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data. Third, it presents a methodology to detect the spatial coordinates of a user's indoor position that outperforms all similar works in this field.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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