Special Issue "3D Indoor Modelling and Navigation"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 June 2017)

Special Issue Editors

Guest Editor
Dr. Sisi Zlatanova

3D Geoinformation, Faculty of Architecture and The Built Environment, Delft University of Technology, Delft, The Netherlands
Website | E-Mail
Interests: 3D GIS; 3D indoor modelling and navigation; CityGML; 3D analysis; emergency response
Guest Editor
Dr. Kourosh Khoshelham

Department of Infrastructure Engineering, University of Melbourne, Melbourne, VIC 3010, Australia
Website | E-Mail
Interests: mapping; spatial data infrastructure; photogrammetry and 3D imaging; machine learning; information extraction from point clouds
Guest Editor
Dr. George Sithole

Division of Geomatics, School of Architecture and Planning and Geomatics, Faculty of Engineering and the Built Environment, University of Cape Town, Private Bag, Rondebosch, 7701, South Africa
Website | E-Mail
Interests: point cloud processing; indoor 3D mapping and modelling; 3D reconstruction

Special Issue Information

Dear Colleagues,

Indoor environments and enclosed spaces have, in recent times, emerged as the next great frontier of mapping. These spaces present challenges that strain mapping technologies that, up until now, have been developed for mapping outdoor spaces. The challenges of mapping enclosed spaces arise from the fact that these spaces are confined, cluttered with people and furniture, and shielded from commonly used positioning systems like GPS, etc.

Today’s maps are commonly used in navigational aids, and the ubiquity of smart mobile devices and more pin-point commercial activity will in future drive a greater demand for indoor navigational aids. As with mapping technologies, indoor navigation is more demanding than its outdoor counterpart. For example, one reason for this is that visibility within indoor environments is limited and additional tools have to be offered to users to overcome this. Solving the challenges has meant that concepts, data models, and standards have had to be redefined to meet the requirements of 3D indoor spatial applications.

The aim of this Special Issue is to present current and state of the art research in the development of acquisition systems, mobile mapping systems, indoor data modelling and analysis, navigation and visualisation of indoor environments. Because of the multidisciplinary nature of the problem of 3D indoor mapping, contributions are expected from various fields including photogrammetry, computer vision and image analysis, laser scanning, data and information management, computer graphics, human–machine interaction and many others.

Topics that will be considered for this issue include (but are not limited to):

  • Indoor acquisition systems
  • Simultaneous localisation and mapping
  • Indoor data structures and models
  • Indoor positioning and localisation
  • Indoor navigation and guidance
  • Navigating between outdoor and indoor models
  • Visualisation and simulation
  • Evacuation and human activity monitoring
  • User requirements and best practices
  • Resolving privacy and legal concerns

Dr. Sisi Zlatanova
Dr. Kourosh Khoshelham
Dr. George Sithole
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (12 papers)

View options order results:
result details:
Displaying articles 1-12
Export citation of selected articles as:

Research

Open AccessArticle On Wi-Fi Model Optimizations for Smartphone-Based Indoor Localization
ISPRS Int. J. Geo-Inf. 2017, 6(8), 233; doi:10.3390/ijgi6080233
Received: 29 June 2017 / Revised: 22 July 2017 / Accepted: 2 August 2017 / Published: 4 August 2017
PDF Full-text (568 KB) | XML Full-text
Abstract
Indoor localization and indoor pedestrian navigation is an active field of research with increasing attention. As of today, many systems will run on commercial smartphones, but most of them still rely on fingerprinting, which demands high setup and maintenance times. Alternatives, such as
[...] Read more.
Indoor localization and indoor pedestrian navigation is an active field of research with increasing attention. As of today, many systems will run on commercial smartphones, but most of them still rely on fingerprinting, which demands high setup and maintenance times. Alternatives, such as simple signal strength prediction models, provide fast setup times, but often do not provide the accuracy required for use cases like indoor navigation or location-based services. While more complex models provide an increased accuracy by including architectural knowledge about walls and other obstacles, they often require additional computation during runtime and demand prior knowledge during setup. Within this work, we will thus focus on simple, easy to set up models and evaluate their performance compared to real-world measurements. The evaluation ranges from a fully-empiric, instant setup, given that the transmitter locations are well known, to a highly optimized scenario based on some reference measurements within the building. Furthermore, we will propose a new signal strength prediction model as a combination of several simple ones. This tradeoff increases accuracy with only minor additional computations. All of the optimized models are evaluated within an actual smartphone-based indoor localization system. This system uses the phone’s Wi-Fi, barometer and IMU to infer the pedestrian’s current location via recursive density estimation based on particle filtering. We will show that while a 100% empiric parameter choice for the model already provides enough accuracy for many use cases, a small number of reference measurements is enough to dramatically increase such a system’s performance. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
Open AccessArticle Robust Indoor Mobile Localization with a Semantic Augmented Route Network Graph
ISPRS Int. J. Geo-Inf. 2017, 6(7), 221; doi:10.3390/ijgi6070221
Received: 9 May 2017 / Revised: 13 July 2017 / Accepted: 17 July 2017 / Published: 19 July 2017
PDF Full-text (3786 KB) | HTML Full-text | XML Full-text
Abstract
In recent years, using smartphones to determine pedestrian locations in indoor environments is an extensively promising technique for improving context-aware applications. However, the applicability and accuracy of the conventional approaches are still limited due to infrastructure-dependence, and there is seldom consideration of the
[...] Read more.
In recent years, using smartphones to determine pedestrian locations in indoor environments is an extensively promising technique for improving context-aware applications. However, the applicability and accuracy of the conventional approaches are still limited due to infrastructure-dependence, and there is seldom consideration of the semantic information inherently existing in maps. In this paper, a semantically-constrained low-complexity sensor fusion approach is proposed for the estimation of the user trajectory within the framework of the smartphone-based indoor pedestrian localization, which takes into account the semantic information of indoor space and its compatibility with user motions. The user trajectory is established by pedestrian dead reckoning (PDR) from the mobile inertial sensors, in which the proposed semantic augmented route network graph with adaptive edge length is utilized to provide semantic constraint for the trajectory calibration using a particle filter algorithm. The merit of the proposed method is that it not only exploits the knowledge of the indoor space topology, but also exhausts the rich semantic information and the user motion in a specific indoor space for PDR accumulation error elimination, and can extend the applicability for diverse pedestrian step length modes. Two experiments are conducted in the real indoor environment to verify of the proposed approach. The results confirmed that the proposed method can achieve highly acceptable pedestrian localization results using only the accelerometer and gyroscope embedded in the phones, while maintaining an enhanced accuracy of 1.23 m, with the indoor semantic information attached to each pedestrian’s motion. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
Figures

Figure 1

Open AccessArticle Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
ISPRS Int. J. Geo-Inf. 2017, 6(7), 213; doi:10.3390/ijgi6070213
Received: 26 May 2017 / Revised: 5 July 2017 / Accepted: 7 July 2017 / Published: 11 July 2017
PDF Full-text (2008 KB) | HTML Full-text | XML Full-text
Abstract
The lonelier evacuees find themselves, the riskier become their wayfinding decisions. This research supports single evacuees in a dynamically changing environment with risk-aware guidance. It deploys the concept of decentralized evacuation, where evacuees are guided by smartphones acquiring environmental knowledge and risk information
[...] Read more.
The lonelier evacuees find themselves, the riskier become their wayfinding decisions. This research supports single evacuees in a dynamically changing environment with risk-aware guidance. It deploys the concept of decentralized evacuation, where evacuees are guided by smartphones acquiring environmental knowledge and risk information via exploration and knowledge sharing by peer-to-peer communication. Peer-to-peer communication, however, relies on the chance that people come into communication range with each other. This chance can be low. To bridge between people being not at the same time at the same places, this paper suggests information depositories at strategic locations to improve information sharing. Information depositories collect the knowledge acquired by the smartphones of evacuees passing by, maintain this information, and convey it to other passing-by evacuees. Multi-agent simulation implementing these depositories in an indoor environment shows that integrating depositories improves evacuation performance: It enhances the risk awareness and consequently increases the chance that people survive and reduces their evacuation time. For evacuating dynamic events, deploying depositories at staircases has been shown more effective than deploying them in corridors. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
Figures

Figure 1

Open AccessArticle A Novel Semantic Matching Method for Indoor Trajectory Tracking
ISPRS Int. J. Geo-Inf. 2017, 6(7), 197; doi:10.3390/ijgi6070197
Received: 15 May 2017 / Revised: 29 June 2017 / Accepted: 29 June 2017 / Published: 1 July 2017
PDF Full-text (4039 KB) | HTML Full-text | XML Full-text
Abstract
The rapid development of smartphone sensors has provided rich indoor pedestrian trajectory data for indoor location-based applications. To improve the quality of these collected trajectory data, map matching methods are widely used to correct trajectories. However, these existing matching methods usually cannot achieve
[...] Read more.
The rapid development of smartphone sensors has provided rich indoor pedestrian trajectory data for indoor location-based applications. To improve the quality of these collected trajectory data, map matching methods are widely used to correct trajectories. However, these existing matching methods usually cannot achieve satisfactory accuracy and efficiency and have difficulty in exploiting the rich information contained in the obtained trajectory data. In this study, we proposed a novel semantic matching method for indoor pedestrian trajectory tracking. Similar to our previous work, pedestrian dead reckoning (PDR) and human activity recognition (HAR) are used to obtain the raw user trajectory data and the corresponding semantic information involved in the trajectory, respectively. To improve the accuracy and efficiency for user trajectory tracking, a semantic-rich indoor link-node model is then constructed based on the input floor plan, in which navigation-related semantics are extracted and formalized for the following trajectory matching. PDR and HAR are further utilized to segment the trajectory and infer the semantics (e.g., “Turn left”, “Turn right”, and “Go straight”). Finally, the inferred semantic information is matched with the semantic-rich indoor link-node model to derive the correct user trajectory. To accelerate the matching process, the semantics inferred from the trajectory are also assigned weights according to their relative importance. The experiments confirm that the proposed method achieves accurate trajectory tracking results while guaranteeing a high matching efficiency. In addition, the resulting semantic information has great application potential in further indoor location-based services. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
Figures

Figure 1

Open AccessArticle A Multiple Ant Colony Optimization Algorithm for Indoor Room Optimal Spatial Allocation
ISPRS Int. J. Geo-Inf. 2017, 6(6), 161; doi:10.3390/ijgi6060161
Received: 4 March 2017 / Revised: 17 May 2017 / Accepted: 24 May 2017 / Published: 1 June 2017
PDF Full-text (2085 KB) | HTML Full-text | XML Full-text
Abstract
Indoor room optimal allocation is of great importance in geographic information science (GIS) applications because it can generate effective indoor spatial patterns that improve human behavior and efficiency. However, few research concerning indoor room optimal allocation has been reported. Using an office building
[...] Read more.
Indoor room optimal allocation is of great importance in geographic information science (GIS) applications because it can generate effective indoor spatial patterns that improve human behavior and efficiency. However, few research concerning indoor room optimal allocation has been reported. Using an office building as an example, this paper presents an integrative approach for indoor room optimal allocation, which includes an indoor room allocation optimization model, indoor connective map design, and a multiple ant colony optimization (MACO) algorithm. The mathematical optimization model is a minimized model that integrates three types of area-weighted costs while considering the minimal requirements of each department to be allocated. The indoor connective map, which is an essential data input, is abstracted by all floor plan space partitions and connectivity between every two adjacent floors. A MACO algorithm coupled with three strategies, namely, (1) heuristic information, (2) two-colony rules, and (3) local search, is effective in achieving a feasible solution of satisfactory quality within a reasonable computation time. A case study was conducted to validate the proposed approach. The results show that the MACO algorithm with these three strategies outperforms other types of ant colony optimization (ACO), Genetic Algorithm (GA), and particle swarm optimization (PSO) algorithms in quality and stability, which demonstrates that the proposed approach is an effective technique for generating optimal indoor room spatial patterns. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
Figures

Figure 1

Open AccessArticle Indoor Fingerprint Positioning Based on Wi-Fi: An Overview
ISPRS Int. J. Geo-Inf. 2017, 6(5), 135; doi:10.3390/ijgi6050135
Received: 18 February 2017 / Revised: 14 April 2017 / Accepted: 25 April 2017 / Published: 28 April 2017
PDF Full-text (871 KB) | HTML Full-text | XML Full-text
Abstract
The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on
[...] Read more.
The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
Figures

Figure 1

Open AccessArticle A Standard Indoor Spatial Data Model—OGC IndoorGML and Implementation Approaches
ISPRS Int. J. Geo-Inf. 2017, 6(4), 116; doi:10.3390/ijgi6040116
Received: 31 December 2016 / Revised: 31 March 2017 / Accepted: 10 April 2017 / Published: 12 April 2017
PDF Full-text (2570 KB) | HTML Full-text | XML Full-text
Abstract
With the recent progress in indoor spatial data modeling, indoor mapping and indoor positioning technologies, several spatial information services for indoor spaces have been provided like for outdoor spaces. In order to support interoperability between indoor spatial information services, IndoorGML was published by
[...] Read more.
With the recent progress in indoor spatial data modeling, indoor mapping and indoor positioning technologies, several spatial information services for indoor spaces have been provided like for outdoor spaces. In order to support interoperability between indoor spatial information services, IndoorGML was published by OGC (Open Geospatial Consortium) as a standard data model and XML-based exchange format. While the previous standards, such as IFC (Industrial Foundation Classes) and CityGML covering also indoor space, aim at feature modeling, the goal of IndoorGML is to establish a standard basis for the indoor space model. As IndoorGML defines a minimum data model for indoor space, more efforts are required to discover its potential aspects, which are not explicitly explained in the standard document. In this paper, we investigate the implications and potential aspects of IndoorGML and its basic concept of the cellular space model and discuss the implementation issues of IndoorGML for several purposes. In particular, we discuss the issues on cell determination, subspacing and the hierarchical structure of indoor space from the IndoorGML viewpoint. Additionally, we also focus on two important issues: computation of indoor distance and the implementation of indoor context-awareness services based on IndoorGML. We expect that this paper will serve as a technical document for better understanding of IndoorGML throughout these discussions. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
Figures

Figure 1

Open AccessArticle Estimation of 3D Indoor Models with Constraint Propagation and Stochastic Reasoning in the Absence of Indoor Measurements
ISPRS Int. J. Geo-Inf. 2017, 6(3), 90; doi:10.3390/ijgi6030090
Received: 31 December 2016 / Revised: 2 March 2017 / Accepted: 13 March 2017 / Published: 21 March 2017
PDF Full-text (5066 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a novel method for the prediction of building floor plans based on sparse observations in the absence of measurements. We derive the most likely hypothesis using a maximum a posteriori probability approach. Background knowledge consisting of probability density functions of
[...] Read more.
This paper presents a novel method for the prediction of building floor plans based on sparse observations in the absence of measurements. We derive the most likely hypothesis using a maximum a posteriori probability approach. Background knowledge consisting of probability density functions of room shape and location parameters is learned from training data. Relations between rooms and room substructures are represented by linear and bilinear constraints. We perform reasoning on different levels providing a problem solution that is optimal with regard to the given information. In a first step, the problem is modeled as a constraint satisfaction problem. Constraint Logic Programming derives a solution which is topologically correct but suboptimal with regard to the geometric parameters. The search space is reduced using architectural constraints and browsed by intelligent search strategies which use domain knowledge. In a second step, graphical models are used for updating the initial hypothesis and refining its continuous parameters. We make use of Gaussian mixtures for model parameters in order to represent background knowledge and to get access to established methods for efficient and exact stochastic reasoning. We demonstrate our approach on different illustrative examples. Initially, we assume that floor plans are rectangular and that rooms are rectangles and discuss more general shapes afterwards. In a similar spirit, we predict door locations providing further important components of 3D indoor models. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
Figures

Figure 1

Open AccessArticle The Performance Analysis of Space Resection-Aided Pedestrian Dead Reckoning for Smartphone Navigation in a Mapped Indoor Environment
ISPRS Int. J. Geo-Inf. 2017, 6(2), 43; doi:10.3390/ijgi6020043
Received: 21 December 2016 / Revised: 26 January 2017 / Accepted: 26 January 2017 / Published: 14 February 2017
PDF Full-text (7628 KB) | HTML Full-text | XML Full-text
Abstract
Smartphones have become indispensable in our daily lives. Their various embedded sensors have inspired innovations in mobile applications—especially for indoor navigation. However, the accuracy, reliability and generalizability of navigation all continue to struggle in environments lacking a Global Navigation Satellite System (GNSS). Pedestrian
[...] Read more.
Smartphones have become indispensable in our daily lives. Their various embedded sensors have inspired innovations in mobile applications—especially for indoor navigation. However, the accuracy, reliability and generalizability of navigation all continue to struggle in environments lacking a Global Navigation Satellite System (GNSS). Pedestrian Dead Reckoning (PDR) is a popular method for indoor pedestrian navigation. Unfortunately, due to its fundamental principles, even a small navigation error will amplify itself, step by step, generally leading to the need for supplementary resources to maintain navigation accuracy. Virtually all mobile devices and most robots contain a basic camera sensor, which has led to the popularity of image-based localization, and vice versa. However, all of the image-based localization requires continuous images for uninterrupted positioning. Furthermore, the solutions provided by either image-based localization or a PDR are usually in a relative coordinate system. Therefore, this research proposes a system, which uses space resection-aided PDR with geo-referenced images of a previously mapped environment to enable seamless navigation and solve the shortcomings of PDR and image-based localization, and evaluates the performance of space resection with different assumptions using a smartphone. The indoor mobile mapping system (IMMS) is used for the effective production of geo-referenced images. The preliminary results indicate that the proposed algorithm is suitable for universal pedestrian indoor navigation, achieving the accuracy required for commercial applications. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
Figures

Figure 1

Open AccessArticle Indoor Multi-Dimensional Location GML and Its Application for Ubiquitous Indoor Location Services
ISPRS Int. J. Geo-Inf. 2016, 5(12), 220; doi:10.3390/ijgi5120220
Received: 18 August 2016 / Revised: 9 November 2016 / Accepted: 24 November 2016 / Published: 29 November 2016
Cited by 1 | PDF Full-text (4945 KB) | HTML Full-text | XML Full-text
Abstract
The Open Geospatial Consortium (OGC) Geography Markup Language (GML) standard provides basic types and a framework for defining geo-informational data models such as CityGML and IndoorGML, which provide standard information models for 3D city modelling and lightweight indoor network navigation. Location information, which
[...] Read more.
The Open Geospatial Consortium (OGC) Geography Markup Language (GML) standard provides basic types and a framework for defining geo-informational data models such as CityGML and IndoorGML, which provide standard information models for 3D city modelling and lightweight indoor network navigation. Location information, which is the semantic engine that fuses big geo-information data, is however, discarded in these standards. The Chinese national standard of Indoor Multi-Dimensional Location GML (IndoorLocationGML) presented in this study can be used in ubiquitous indoor location intelligent applications for people and robots. IndoorLocationGML is intended as an indoor multi-dimensional location information model and exchange data format standard, mainly for indoor positioning and navigation. This paper introduces the standard’s main features: (1) terminology; (2) indoor location information model using a Unified Modeling Language (UML) class diagram; (3) indoor location information markup language based on GML; and (4) use cases. A typical application of the standard is then discussed. This standard is applicable to the expression, storage, and distribution of indoor multi-dimensional location information, and to the seamless integration of indoor–outdoor location information. The reference and basis are therefore relevant to publishers, managers, users, and developers of indoor navigation and location-based services (LBS). Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
Figures

Figure 1

Open AccessArticle Indexing for Moving Objects in Multi-Floor Indoor Spaces That Supports Complex Semantic Queries
ISPRS Int. J. Geo-Inf. 2016, 5(10), 176; doi:10.3390/ijgi5100176
Received: 17 May 2016 / Revised: 8 September 2016 / Accepted: 18 September 2016 / Published: 27 September 2016
PDF Full-text (14931 KB) | HTML Full-text | XML Full-text
Abstract
With the emergence of various types of indoor positioning technologies (e.g., radio-frequency identification, Wi-Fi, and iBeacon), how to rapidly retrieve indoor cells and moving objects has become a key factor that limits those indoor applications. Euclidean distance-based indexing techniques for outdoor moving objects
[...] Read more.
With the emergence of various types of indoor positioning technologies (e.g., radio-frequency identification, Wi-Fi, and iBeacon), how to rapidly retrieve indoor cells and moving objects has become a key factor that limits those indoor applications. Euclidean distance-based indexing techniques for outdoor moving objects cannot be used in indoor spaces due to the existence of indoor obstructions (e.g., walls). In addition, currently, the indexing of indoor moving objects is mainly based on space-related query and less frequently on semantic query. To address these two issues, the present study proposes a multi-floor adjacency cell and semantic-based index (MACSI). By integrating the indoor cellular space with the semantic space, the MACSI subdivides open cells (e.g., hallways and lobbies) using space syntax and optimizes the adjacency distances between three-dimensionally connected cells (e.g., elevators and stairs) based on the caloric cost that extends single floor indoor space to three dimensional indoor space. Moreover, based on the needs of semantic query, this study also proposes a multi-granularity indoor semantic hierarchy tree and establishes semantic trajectories. Extensive simulation and real-data experiments show that—compared with the indoor trajectories delta tree (ITD-tree) and the semantic-based index (SI)—the MACSI produces more reliable query results with significantly higher semantic query and update efficiencies; has superior semantic expansion capability; and supports multi-granularity complex semantic queries. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)
Figures

Figure 1

Open AccessArticle WiGeR: WiFi-Based Gesture Recognition System
ISPRS Int. J. Geo-Inf. 2016, 5(6), 92; doi:10.3390/ijgi5060092
Received: 11 February 2016 / Revised: 28 May 2016 / Accepted: 6 June 2016 / Published: 14 June 2016
Cited by 1 | PDF Full-text (4938 KB) | HTML Full-text | XML Full-text
Abstract
Recently, researchers around the world have been striving to develop and modernize human–computer interaction systems by exploiting advances in modern communication systems. The priority in this field involves exploiting radio signals so human–computer interaction will require neither special devices nor vision-based technology. In
[...] Read more.
Recently, researchers around the world have been striving to develop and modernize human–computer interaction systems by exploiting advances in modern communication systems. The priority in this field involves exploiting radio signals so human–computer interaction will require neither special devices nor vision-based technology. In this context, hand gesture recognition is one of the most important issues in human–computer interfaces. In this paper, we present a novel device-free WiFi-based gesture recognition system (WiGeR) by leveraging the fluctuations in the channel state information (CSI) of WiFi signals caused by hand motions. We extract CSI from any common WiFi router and then filter out the noise to obtain the CSI fluctuation trends generated by hand motions. We design a novel and agile segmentation and windowing algorithm based on wavelet analysis and short-time energy to reveal the specific pattern associated with each hand gesture and detect duration of the hand motion. Furthermore, we design a fast dynamic time warping algorithm to classify our system’s proposed hand gestures. We implement and test our system through experiments involving various scenarios. The results show that WiGeR can classify gestures with high accuracy, even in scenarios where the signal passes through multiple walls. Full article
(This article belongs to the Special Issue 3D Indoor Modelling and Navigation)

Journal Contact

MDPI AG
IJGI Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to IJGI Edit a special issue Review for IJGI
logo
loading...
Back to Top