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Article

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

Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45221-0030, USA
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Author to whom correspondence should be addressed.
Academic Editor: Spyros Panagiotakis
Information 2021, 12(3), 114; https://doi.org/10.3390/info12030114
Received: 24 January 2021 / Revised: 4 March 2021 / Accepted: 5 March 2021 / Published: 7 March 2021
(This article belongs to the Special Issue Pervasive Computing in IoT)
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. View Full-Text
Keywords: big data; machine learning; indoor localization; ambient assisted living; internet of things; smart homes; elderly population; indoor location; human–computer interaction; assistive technology big data; machine learning; indoor localization; ambient assisted living; internet of things; smart homes; elderly population; indoor location; human–computer interaction; assistive technology
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MDPI and ACS Style

Thakur, N.; Han, C.Y. Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes. Information 2021, 12, 114. https://doi.org/10.3390/info12030114

AMA Style

Thakur N, Han CY. Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes. Information. 2021; 12(3):114. https://doi.org/10.3390/info12030114

Chicago/Turabian Style

Thakur, Nirmalya, and Chia Y. Han. 2021. "Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes" Information 12, no. 3: 114. https://doi.org/10.3390/info12030114

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