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An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons
2019
Sensors
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Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m. ...
To the best of the authors' knowledge, this is the first work concerning indoor localization of consumers in a real retail store. ...
On the contrary, for the random forest classifier, in 80% of the cases the localization error is approximately 2.5 m. ...
doi:10.3390/s19204550
fatcat:6wkyek6wsrhgborokt67f65rti
Indoor Position Detection Using Smartwatch and Beacons
2020
Sensors and materials
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Various useful screens with easy-to-understand visualizations are provided for monitoring subject behaviors and time spent in certain areas, giving a summary of indoor positioning. ...
Data mining techniques were applied to classify indoor positioning zones. A noise reduction process combining two data smoothing techniques was incorporated. ...
Anthony French for English proofreading and all the volunteers involved in our data collection. ...
doi:10.18494/sam.2020.2386
fatcat:oevrjrrhc5fhrhlbewldab3sj4
Visible light communication-based positioning for indoor environments using supervised learning
2020
GLOBECOM 2020 - 2020 IEEE Global Communications Conference
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Then, the CSI of the VLC receiver is used to train a Random Forest classifier, which will predict the position of the object during the assessment phase. ...
paper studies a novel way to estimate the position of an object in an indoor environment, using the Channel State Information (CSI) that a Visible Light Communication (VLC) system collects to maintain ...
RANDOM FOREST ALGORITHM Random Forest [17] is a supervised learning algorithm in which a collective learning process is carried out in a group of decision trees. ...
doi:10.1109/globecom42002.2020.9322577
fatcat:yfq2vfds3ba33pdu7urvpr5ncq
Towards an Indoor Location System using Indoor-light as Information Source
2015
Research in Computing Science
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In this paper, we present an alternative approach based on the use of indoor lighting variations, feature extraction of the signal and a deterministic selection to get a model in order to estimate localization ...
of an individual in indoor environments. ...
using well-known Random Forest Classifier [3] . ...
doi:10.13053/rcs-91-1-1
fatcat:l4pdeynd55eexlhnu44toryo2a
Mixed-Mode Wireless Indoor Positioning System Using Proximity Detection and Database Correlation
2014
Proceedings of the 2014 Federated Conference on Computer Science and Information Systems
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The paper presents a prototype mixed-mode wireless indoor positioning and navigation system. ...
The radio nodes of the network can operate in two power modes providing basis for both rough and precise user positioning. ...
The experiments conducted in a large office building resulted in average positioning error not exceeding 2.32 meters when Random Forest classifier was used with combined proximity sensing and database ...
doi:10.15439/2014f328
dblp:conf/fedcsis/KorbelWSP14
fatcat:3zsrbyc5ejg2bdo4f2g3u4i3bu
Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes
2021
Information
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This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. ...
indoor location in a specific 'activity-based zone' during Activities of Daily Living. ...
We did not develop the RapidMiner "process" by using the Random Forest approach in this section as we had already developed the same in Figure 12 and discussed its performance characteristics in terms ...
doi:10.3390/info12030114
fatcat:ankfyi77inhqxg22fra3rcgzxq
Random Forest and Interpolation Techniques for Fingerprint-based Indoor Positioning System in Un-ideal Environment
2021
International Journal of Computing and Digital Systems
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We conducted a measurement campaign in an unideal indoor environment to see how far our proposed method can still handle the fluctuated values of RSSI. ...
However, in almost all scenarios, the Random Forest can better perform MED in terms of decreasing the maximum estimated error. ...
Received Signal Strength Indicator (RSSI) In a simple indoor localization/IPS, we often find the straightforward use of RSSI. ...
doi:10.12785/ijcds/100166
fatcat:25p4wdsavnbyxowgcdqi2rqppu
Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning
2021
Sensors
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The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. ...
In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%. ...
Introduction Indoor localization, as defined in [1] , is a process that is employed to obtain a user or device location in an indoor environment. ...
doi:10.3390/s21134605
fatcat:2tnufajpqrdgblcqemdty3pj24
A Generalized Model for Indoor Location Estimation Using Environmental Sound from Human Activity Recognition
2018
ISPRS International Journal of Geo-Information
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A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user. ...
Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve ...
activity that is done in a certain room in an indoor environment. ...
doi:10.3390/ijgi7030081
fatcat:jky5x337gfdnlafih7jdhl4dbq
A Survey of Machine Learning for Indoor Positioning
2020
IEEE Access
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Supervised Learning: Random Forest Random forest algorithm is a collection of number of Decision Trees and each tree in the forest gives a classification. ...
In this case Random Forest can be used to eliminate the over-fitting problem. ...
doi:10.1109/access.2020.3039271
fatcat:htzgf2mwp5gmjbx3cczg5rl7ru
Towards Indoor Transportation Mode Detection Using Mobile Sensing
[chapter]
2015
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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used indoors, especially in large building complexes. ...
In addition, we explore methods for transportation mode detection we deem suitable in indoor settings, and we perform an extensive real-world evaluation of (combinations of) such methods at a large hospital ...
We see that random forests seem to be superior to support vector machines and K-nearest neighbour classifiers in this setup, while all are superior to the C4.5 classifiers. ...
doi:10.1007/978-3-319-29003-4_15
fatcat:vwxy44dm45djzcz243gkomot4i
Visible light communication-based monitoring for indoor environments using unsupervised learning
[article]
2021
arXiv
pre-print
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This way, different clusters can be created on the collected CSI data, which could be then mapped into relevant events to-be-monitored in the indoor environments, such as the presence of a new object in ...
When compared to supervised learning algorithms, the proposed approach does not need to add tags in the training data, simplifying notably the implementation of the machine learning classifier. ...
For example, the authors of [7] , [8] presented an object identification and object localization algorithm, respectively, using a Random Forest Classifier that was trained using the Channel State Information ...
arXiv:2101.10838v1
fatcat:4jkwwknncjcexngb2k3ni54yca
A Model to Support Fluid Transitions between Environments for Mobile Augmented Reality Applications
2019
Sensors
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To assess the model, we developed a MAR application and conducted a navigation test with volunteers to validate transitions between outdoor and indoor environments, followed by a short interview. ...
The transition between environments is hard because there are currently no localization techniques that work well in any place: sensor-based applications can be harmed by obstacles that hamper sensor communication ...
In all three cases, the Random Forest classifier performed better, even with a low number of random trees (20) , thus we chose this classifier in the implementation of the application prototype. ...
doi:10.3390/s19194254
fatcat:skt4bbj6ljc2jbjxhcbanfxxta
MapSense
2020
ACM Transactions on Internet of Things
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To model the indoor environments in a proper manner, novel data acquisition concepts and data modeling algorithms have been devised to meet the requirements of indoor spatial applications. ...
Indoor environments de facto differ from outdoor spaces in two aspects: spaces are smaller and there are many structural objects such as walls, doors, and furniture. ...
Moreover, ray tracing and random forest methods are used for detecting and classifying other objects, e.g. doors, tables, and chairs. (4) We define a formal grammar through which, in a second step, the ...
doi:10.1145/3379342
dblp:journals/tiot/AbdelaalSDRBF20
fatcat:yovxy2thebd3ncmsg5n4xpsyee
Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks
[article]
2021
arXiv
pre-print
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In such an environment, indoor localization becomes one of the essential services, and the indoor localization systems to be deployed should be scalable enough to cover the expected expansion of those ...
In this paper, we propose hierarchical multi-building and multi-floor indoor localization based on a recurrent neural network (RNN) using Wi-Fi fingerprinting, eliminating the need of complicated data ...
ACKNOWLEDGMENT This work was supported in part by Postgraduate Research Scholarships (under Grant PGRS1912001) and Key Program Special Fund (under Grant KSF-E-25) of Xi'an Jiaotong-Liverpool University ...
arXiv:2112.12478v1
fatcat:a6r3ujnrmbd75mmaihxvurisle
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