Calibrating Recurrent Neural Networks on Smartphone Inertial Sensors for Location Tracking

Wei, Xijia; Radu, Valentin

doi:10.1109/ipin.2019.8911768

Cited by 9 publications

(13 citation statements)

References 23 publications

(23 reference statements)

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“…in Table 4 indicates whether a work allows for an arbitrary placement of the device or not. Only two works specify that they support an arbitrary placement of the smartphone [47], [108], which shows that smartphone-based localization is still a challenge. In fact, 54% of the reviewed works do not specify where the device is located.…”

Section: A Sensing Devices and Their Placementmentioning

confidence: 99%

Survey of Machine Learning Methods Applied to Urban Mobility

Ahmed

Diaz

2022

IEEE Access

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To increase the sustainability in urban mobility, it is necessary to optimally combine public and shared vehicles throughout a passenger's trip. In this work, we present a survey on urban mobility based on passengers' data and machine learning methods. We focus on four applications for urban mobility: public datasets, passenger localization, detection of the transport mode and pattern recognition and generation of mobility models. Public datasets lack data of multimodal trips and are in need of guidelines to facilitate the data collection and documentation processes. Passenger localization is predominantly done through fingerprinting in indoor environments; and fingerprinting relies on unsupervised learning is used to survey access points. The most common mean of transport detected is the bus, followed by walking and biking, while e-scooters are not included within the detected transport modes. The existing works focus on predicting the travel time of the passenger's trajectory and no machine learning method stands out to estimate the travel time. There is still a need for works that analyze how passengers make use of the urban infrastructure, which will support municipalities and transport mode operators in resource planning and service design.

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Section: A Sensing Devices and Their Placementmentioning

confidence: 99%

Survey of Machine Learning Methods Applied to Urban Mobility

Ahmed

Diaz

2022

IEEE Access

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“…These applications are trained and tested on the data obtained from single buildings such as universities [26], [27] or the well known UJI Indoor Localization dataset [15], [21] that provides real life data from multiple floors.…”

Section: Related Workmentioning

confidence: 99%

WiFi Based Distance Estimation Using Supervised Machine Learning

Kahraman¹,

Kostas²,

Zampella³

et al. 2022

Preprint

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In recent years WiFi became the primary source of information to locate a person or device indoor. Collecting RSSI values as reference measurements with known positions, known as WiFi fingerprinting, is commonly used in various positioning methods and algorithms that appear in literature. However, measuring the spatial distance between given set of WiFi fingerprints is heavily affected by the selection of the signal distance function used to model signal space as geospatial distance. In this study, the authors proposed utilization of machine learning to improve the estimation of geospatial distance between fingerprints. This research examined data collected from 13 different open datasets to provide a broad representation aiming for general model that can be used in any indoor environment. The proposed novel approach extracted data features by examining a set of commonly used signal distance metrics via feature selection process that includes feature analysis and genetic algorithm. To demonstrate that the output of this research is venue independent, all models were tested on datasets previously excluded during the training and validation phase. Finally, various machine learning algorithms were compared using wide variety of evaluation metrics including ability to scale out the test bed to real world unsolicited datasets.

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“…A recent and promising application of ML is for tracking or displacement estimation by regression. For example, the authors in [138] use a deep RNN-LSTM to estimate a smartphone location and to track it. They use multiple previous estimations obtained only from the inertial data to make the localization prediction.…”

Section: Machine Learning In Inertial Navigation Systemsmentioning

confidence: 99%

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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With the popularization of machine learning (ML) techniques and the increased chipset's performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

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Calibrating Recurrent Neural Networks on Smartphone Inertial Sensors for Location Tracking

Cited by 9 publications

References 23 publications

Survey of Machine Learning Methods Applied to Urban Mobility

Survey of Machine Learning Methods Applied to Urban Mobility

WiFi Based Distance Estimation Using Supervised Machine Learning

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

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