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Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks

1
School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
2
Department of Computer Science, University of Sheffield, Sheffield S1 4DP, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Marcin Grzegorzek and Frank Deinzer
Sensors 2021, 21(22), 7488; https://doi.org/10.3390/s21227488
Received: 1 September 2021 / Revised: 13 October 2021 / Accepted: 14 October 2021 / Published: 11 November 2021
(This article belongs to the Special Issue Multisensors Indoor Localization)
Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localization using smartphone sensors. However, specialising these solutions for difficult edge cases remains challenging. Here we propose an end-to-end hybrid multimodal deep neural network localization system, MM-Loc, relying on zero hand-engineered features, but learning automatically from data instead. This is achieved by using modality-specific neural networks to extract preliminary features from each sensing modality, which are then combined by cross-modality neural structures. We show that our choice of modality-specific neural architectures can estimate the location independently. But for better accuracy, a multimodal neural network that fuses the features of early modality-specific representations is a better proposition. Our proposed MM-Loc system is tested on cross-modality samples characterised by different sampling rate and data representation (inertial sensors, magnetic and WiFi signals), outperforming traditional approaches for location estimation. MM-Loc elegantly trains directly from data unlike conventional indoor positioning systems, which rely on human intuition. View Full-Text
Keywords: indoor localization; sensor fusion; multimodal deep neural network; multimodal sensing; wifi fingerprinting; pedestrian dead reckoning indoor localization; sensor fusion; multimodal deep neural network; multimodal sensing; wifi fingerprinting; pedestrian dead reckoning
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MDPI and ACS Style

Wei, X.; Wei, Z.; Radu, V. Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks. Sensors 2021, 21, 7488. https://doi.org/10.3390/s21227488

AMA Style

Wei X, Wei Z, Radu V. Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks. Sensors. 2021; 21(22):7488. https://doi.org/10.3390/s21227488

Chicago/Turabian Style

Wei, Xijia, Zhiqiang Wei, and Valentin Radu. 2021. "Sensor-Fusion for Smartphone Location Tracking Using Hybrid Multimodal Deep Neural Networks" Sensors 21, no. 22: 7488. https://doi.org/10.3390/s21227488

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