Data fusion for robust indoor localisation in digital health

Kozłowski, Michał; Byrne, Dallan; Santos-Rodríguez, Raúl; Piechocki, Robert J.

doi:10.1109/wcncw.2018.8369009

Cited by 13 publications

(32 citation statements)

References 22 publications

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“…Signatures from different sections of the human body were found to differ both, in the way they are exerted and their own estimation potential as per Bao et al [10]. In our own study [78] we considered wrist-worn accelerometer as a complementary source of information in indoor location estimation. This method aimed to robustify the localisation performance by assuming that humans have a tendency of performing similar tasks in similar places in a house.…”

Section: Inertial Sensorsmentioning

confidence: 92%

“…that is, minimisation of the absolute Euclidean error between the prediction and label. Whilst there exist other metrics of evaluation [78], Euclidean error is by far the most popular [97], and is used extensively throughout this study.…”

Section: The Task Of Probabilistic Localisationmentioning

confidence: 99%

“…They are both, however, prominently used as part of Inertial Navigation Systems (INS), which constitute the focus of many localisation-centric research enquiries. There is a large body of literature pertaining to inertial sensing for localisation [2,12,28,54,78,100]. They are particularly popular as part of the Pedestrian Dead Reckoning (PDR) applications [12,67,183].…”

Section: Inertial Sensorsmentioning

confidence: 99%

“…Indoor localisation has been regularly cited as an important ambition of many fields in both, academia and industry. The use cases include pervasive health monitoring [13,78], targeted advertising [9], factory vehicle tracking [71] and robotics [15,90], amongst others. However, implementations of localisation methods and algorithms differ, depending on the need, deployment methods, available utilities, resources and sensors [54,181].…”

Section: Introductionmentioning

confidence: 99%

“…These sensors are used to achieve target tracking and localisation. Additionally, we provide an overview of favoured fusion mechanisms employed to achieve higher accuracy [182], efficiency [83], robustness [57], or combination thereof [78]. The utilisation of these sensors is highly dependent on the use case.…”

Section: Introductionmentioning

confidence: 99%

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Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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Section: Inertial Sensorsmentioning

confidence: 92%

Section: The Task Of Probabilistic Localisationmentioning

confidence: 99%