Indoor localization in a hospital environment using Random Forest classifiers

Calderoni, Luca; Ferrara, Matteo; Franco, Annalisa; Maio, Dario

doi:10.1016/j.eswa.2014.07.042

Cited by 139 publications

(73 citation statements)

References 10 publications

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“…[9], [10], [11], [12]). However, as Kjaergaard et al point out in [13], 'While much effort has gone into achieving high positioning accuracy... it is our contention that the general problem is not sufficiently well understood, thus preventing deployments and their usage by applications to become more widespread'.…”

Section: Introductionmentioning

confidence: 99%

Navigating in large hospitals

Hughes

Pinchin

Brown

et al. 2015

2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Abstract-Navigating around large hospitals can be a stressful and time-consuming experience for all users of the hospital infrastructure. Navigation difficulties encountered by patients and visitors can result in missed appointments or simply create a poor impression of the hospital organisation. When staff encounter navigation difficulties this can lead to cost and efficiency issues and potentially put patient safety at risk. Despite the provision of an array of in-hospital navigational aids, 'getting lost' continues to be an everyday problem in these large complex environments.This study aims to to identify factors which affect navigation in hospitals. We do not seek to evaluate the effectiveness of a single navigation aid, instead the objective of this study was to understand the environment in which a new system must operate and the gaps in provision left by existing navigation aids. This study is intended to be used to inform the development of new in-hospital navigational aids, be they technological or otherwise.

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Section: Introductionmentioning

confidence: 99%

Navigating in large hospitals

Hughes

Pinchin

Brown

et al. 2015

2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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“…In fact, a spectacular growth of studies about indoor location has been witnessed since RADAR [3] was proposed in 2000. The ubiquity of Wi-Fi Internet connectivity and smartphones make Wi-Fi-based positioning very popular [3,41,17,22,60,47,49,11,39,42], despite the diversity of the technologies available for indoor positioning: Radio-Frequency Identification (RFID) [33,51,9], Bluetooth [20,44], ZigBee [50], Ultrasound [30,26,55], Magnetic field variations [14,27], LED light [38,34], Ultra Wide Band [25,23], and Hybrid solutions [32], among others. The IPSs based on Wi-Fi fingerprinting techniques are preferred to those based on the propagation model, angle of arrival, time of arrival and time difference of arrival because they do not require any very specialized hardware, line-of-sight to the emitter, or knowledge about the precise location of radio emitters to operate [71,70].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the newest applications need to know the user's location to customize their services [19,54,62], monitor people [9], or track Internet-of-Things objects [42], among others. Moreover, the location can also be used for detecting the user's activities and to provide services based on them.…”

Section: Introductionmentioning

confidence: 99%

“…The location of a device is commonly determined by computing the distance or the similarity between a fingerprint collected by the device and the fingerprints contained in the reference database [49]. Wi-Fi fingerprinting is a complex subject which can profit from well-established Expert System techniques by implementing advanced machine learning techniques (Bayesian Inference [72], Neural Networks [37,10], Decision Trees [69], and Random Forest [9], among others).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL–ETRI competition

Torres-Sospedra

Moreira

Knauth

et al. 2017

AIS

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Abstract. This paper presents results from comparing different Wi-Fi fingerprinting algorithms on the same private dataset. The algorithms where realized by independent teams in the frame of the off-site track of the EvAAL-ETRI Indoor Localization Competition which was part of the Sixth International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015). Competitors designed and validated their algorithms against the publicly available UJIIndoorLoc database which contains a huge reference-and validation data set. All competing systems were evaluated using the mean error in positioning, with penalties, using a private test dataset. The authors believe that this is the first work in which Wi-Fi fingerprinting algorithm results delivered by several independent and competing teams are fairly compared under the same evaluation conditions. The analysis also comprises a combined approach: Results indicate that the competing systems where complementary, since an ensemble that combines three competing methods reported the overall best results.

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“…The challenges of future mobility ecosystems in many aspects such as transportation, environmental sustainability and human well-being call for novel approaches to universal positioning and contextual thinking [1,2].…”

Section: Introductionmentioning

confidence: 99%

Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution

et al. 2015

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Abstract:The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral synthesis of geographical location, human motion state and mobility context. A geo-context computing solution consists of a positioning engine, a motion state recognition engine, and a context inference component. In the geo-context concept, the human motion states and mobility context are associated with the geographical location where they occur. A hybrid geo-context computing solution is implemented that runs on a smartphone, and it utilizes measurements of multiple sensors and signals of opportunity that are available within a smartphone. Pedestrian location and motion states are estimated jointly under the framework of hidden Markov models, and they are used in a reciprocal manner to improve their estimation performance of one another. It is demonstrated that pedestrian location estimation has better accuracy when its motion state is known, and in turn, the performance of motion state recognition can be improved with increasing reliability when the location is given. The geo-context inference is implemented simply with the expert system principle, and more sophisticated approaches will be developed. OPEN ACCESSMicromachines 2015, 6 700

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Indoor localization in a hospital environment using Random Forest classifiers

Cited by 139 publications

References 10 publications

Navigating in large hospitals

Navigating in large hospitals

A realistic evaluation of indoor positioning systems based on Wi-Fi fingerprinting: The 2015 EvAAL–ETRI competition

Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution

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