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Special Issue "Sensors and Sensing in Indoor Localization, Tracking, Navigation and Activity Monitoring"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 30 September 2017

Special Issue Editors

Guest Editor
Prof. Dr. Jesús Ureña

Department of Electronics, School of Engineering, University of Alcala, Campus Universitario s/n, 28805 Alcala de Henares, Madrid, Spain
Website | E-Mail
Interests: ultrasonic signal processing; Local Positioning Systems (LPSs); mobile robots;, electronic control, tracking and navigation; daily live monitoring; algorithm implementation on software and hardware
Guest Editor
Dr. Álvaro Hernández Alonso

Department of Electronics, School of Engineering, University of Alcala, Campus Universitario s/n, E-28805 Alcala de Henares, Madrid, Spain
E-Mail
Interests: ultrasonic sensory systems; Local Positioning Systems (LPSs); embedded systems; electronic design
Guest Editor
Dr. Juan Jesús García Domínguez

Department of Electronics, School of Engineering, University of Alcala, Campus Universitario s/n, 28805 Alcala de Henares, Madrid, Spain
E-Mail
Interests: ^local positioning systems; pedestrian dead reckoning; daily live monitoring; body sensor networks

Special Issue Information

Dear Colleagues,

Indoor localization has become a key issue for emerging location-based applications (LBA) in many activity areas. Different technologies and strategies, sometimes including their fusion, compete and/or collaborate to provide a solution to the indoor localization problem. Most times, the selection of a certain system depends on the final application that imposes different constraints, such as accuracy, granularity, coverage area, ease of deployment, calibration and reconfiguration, cost, etc. Large-scale deployment of such location systems needs a network support for different aspects: availability and accessibility, configurability and scalability, privacy, security, etc. Definitely, this strategic topic will lead to technological innovations that have a great impact on the daily activities of people in the coming years, in areas such as health and independent living, home and building automation, leisure, security, etc.

This Special Issue is devoted to new research results and developments in the area of sensors and technologies for Indoor Localization Systems (ILS), which include RF, IR, ultrasonic, magnetic, optical, inertial or other particular systems, as well as the positioning strategies and algorithms, including sensor fusion and networking. As the final application may play an important role from the very initial stages of the system design and deployment, we are also interested in ILS applications, for instance in object tracking, navigation of robots or people, and activity monitoring.

Prof. Dr. Jesús Ureña
Dr. Álvaro Hernández Alonso
Dr. Juan Jesús García Domínguez
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. Sensors 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 1800 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.

Keywords

  • Sensors and technologies for Indoor Localization Systems (ILS)
  • Positioning strategies and algorithms; sensor fusion and networking.
  • System deployment and maintenance
  • Mobile Robot Navigation with ILS
  • Object tracking
  • People assistance and activity monitoring
  • Other ILS applications

Published Papers (13 papers)

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Research

Open AccessArticle LOCALI: Calibration-Free Systematic Localization Approach for Indoor Positioning
Sensors 2017, 17(6), 1213; doi:10.3390/s17061213 (registering DOI)
Received: 15 March 2017 / Revised: 20 May 2017 / Accepted: 22 May 2017 / Published: 25 May 2017
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Abstract
Recent advancements in indoor positioning systems are based on infrastructure-free solutions, aimed at improving the location accuracy in complex indoor environments without the use of specialized resources. A popular infrastructure-free solution for indoor positioning is a calibration-based positioning, commonly known as fingerprinting. Fingerprinting
[...] Read more.
Recent advancements in indoor positioning systems are based on infrastructure-free solutions, aimed at improving the location accuracy in complex indoor environments without the use of specialized resources. A popular infrastructure-free solution for indoor positioning is a calibration-based positioning, commonly known as fingerprinting. Fingerprinting solutions require extensive and error-free surveys of environments to build radio-map databases, which play a key role in position estimation. Fingerprinting also requires random updates of the database, when there are significant changes in the environment or a decrease in the accuracy. The calibration of the fingerprinting database is a time-consuming and laborious effort that prevents the extensive adoption of this technique. In this paper, we present a systematic LOCALIzation approach, “LOCALI”, for indoor positioning, which does not require a calibration database and extensive updates. The LOCALI exploits the floor plan/wall map of the environment to estimate the target position by generating radio maps by integrating path-losses over certain trajectories in complex indoor environments, where triangulation using time information or the received signal strength level is highly erroneous due to the fading effects caused by multi-path propagation or absorption by environmental elements or varying antenna alignment. Experimental results demonstrate that by using the map information and environmental parameters, a significant level of accuracy in indoor positioning can be achieved. Moreover, this process requires considerably lesser effort compared to the calibration-based techniques. Full article
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Open AccessArticle A Novel Real-Time Reference Key Frame Scan Matching Method
Sensors 2017, 17(5), 1060; doi:10.3390/s17051060
Received: 18 March 2017 / Revised: 23 April 2017 / Accepted: 3 May 2017 / Published: 7 May 2017
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Abstract
Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global
[...] Read more.
Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems. Full article
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Open AccessArticle Orthogonal Chirp-Based Ultrasonic Positioning
Sensors 2017, 17(5), 976; doi:10.3390/s17050976
Received: 15 March 2017 / Revised: 18 April 2017 / Accepted: 24 April 2017 / Published: 27 April 2017
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Abstract
This paper presents a chirp based ultrasonic positioning system (UPS) using orthogonal chirp waveforms. In the proposed method, multiple transmitters can simultaneously transmit chirp signals, as a result, it can efficiently utilize the entire available frequency spectrum. The fundamental idea behind the proposed
[...] Read more.
This paper presents a chirp based ultrasonic positioning system (UPS) using orthogonal chirp waveforms. In the proposed method, multiple transmitters can simultaneously transmit chirp signals, as a result, it can efficiently utilize the entire available frequency spectrum. The fundamental idea behind the proposed multiple access scheme is to utilize the oversampling methodology of orthogonal frequency-division multiplexing (OFDM) modulation and orthogonality of the discrete frequency components of a chirp waveform. In addition, the proposed orthogonal chirp waveforms also have all the advantages of a classical chirp waveform. Firstly, the performance of the waveforms is investigated through correlation analysis and then, in an indoor environment, evaluated through simulations and experiments for ultrasonic (US) positioning. For an operational range of approximately 1000 mm, the positioning root-mean-square-errors (RMSEs) &90% error were 4.54 mm and 6.68 mm respectively. Full article
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Open AccessArticle Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network
Sensors 2017, 17(5), 969; doi:10.3390/s17050969
Received: 18 January 2017 / Revised: 20 April 2017 / Accepted: 21 April 2017 / Published: 27 April 2017
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Abstract
Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is
[...] Read more.
Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is an energy-intensive task, especially when the RF sensors work in traditional work mode, in which the sensors directly send raw RSS measurements of all links to a base station (BS). The traditional work mode is unfavorable for the power constrained RF sensors because the amount of data delivery increases dramatically as the number of sensors grows. In this paper, we propose a binary work mode in which RF sensors send the link states instead of raw RSS measurements to the BS, which remarkably reduces the amount of data delivery. Moreover, we develop two localization methods for the binary work mode which corresponds to stationary and moving target, respectively. The first localization method is formulated based on grid-based maximum likelihood (GML), which is able to achieve global optimum with low online computational complexity. The second localization method, however, uses particle filter (PF) to track the target when constant snapshots of link stats are available. Real experiments in two different kinds of environments were conducted to evaluate the proposed methods. Experimental results show that the localization and tracking performance under the binary work mode is comparable to the those in traditional work mode while the energy efficiency improves considerably. Full article
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Open AccessArticle Platform Architecture for Decentralized Positioning Systems
Sensors 2017, 17(5), 957; doi:10.3390/s17050957
Received: 13 February 2017 / Revised: 7 April 2017 / Accepted: 19 April 2017 / Published: 26 April 2017
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Abstract
A platform architecture for positioning systems is essential for the realization of a flexible localization system, which interacts with other systems and supports various positioning technologies and algorithms. The decentralized processing of a position enables pushing the application-level knowledge into a mobile station
[...] Read more.
A platform architecture for positioning systems is essential for the realization of a flexible localization system, which interacts with other systems and supports various positioning technologies and algorithms. The decentralized processing of a position enables pushing the application-level knowledge into a mobile station and avoids the communication with a central unit such as a server or a base station. In addition, the calculation of the position on low-cost and resource-constrained devices presents a challenge due to the limited computing, storage capacity, as well as power supply. Therefore, we propose a platform architecture that enables the design of a system with the reusability of the components, extensibility (e.g., with other positioning technologies) and interoperability. Furthermore, the position is computed on a low-cost device such as a microcontroller, which simultaneously performs additional tasks such as data collecting or preprocessing based on an operating system. The platform architecture is designed, implemented and evaluated on the basis of two positioning systems: a field strength system and a time of arrival-based positioning system. Full article
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Open AccessArticle An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study
Sensors 2017, 17(5), 951; doi:10.3390/s17050951
Received: 14 March 2017 / Revised: 20 April 2017 / Accepted: 20 April 2017 / Published: 26 April 2017
PDF Full-text (1204 KB) | HTML Full-text | XML Full-text
Abstract
Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based
[...] Read more.
Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based on fusing trilateration and dead reckoning. We employ Kalman filtering as a position fusion algorithm. Moreover, we adopt an Android device with Bluetooth Low Energy modules as the communication platform to avoid excessive energy consumption and to improve the stability of the received signal strength. To further improve the positioning accuracy, we take the environmental context information into account while generating the position fixes. Extensive experiments in a testbed are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. Additionally, the influence of the knowledge of the environmental context is also examined. Finally, our proposed fusion method outperforms both trilateration and dead reckoning in terms of accuracy: experimental results show that the Kalman-based fusion, for our settings, achieves a positioning accuracy of less than one meter. Full article
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Open AccessArticle Using Dimensionality Reduction Techniques for Refining Passive Indoor Positioning Systems Based on Radio Fingerprinting
Sensors 2017, 17(4), 871; doi:10.3390/s17040871
Received: 31 January 2017 / Revised: 3 April 2017 / Accepted: 12 April 2017 / Published: 15 April 2017
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Abstract
Indoor positioning methods based on fingerprinting and radio signals rely on the quality of the radio map. For example, for room-level classification purposes, it is required that the signal observations related to each room exhibit significant differences in their RSSI values. However, it
[...] Read more.
Indoor positioning methods based on fingerprinting and radio signals rely on the quality of the radio map. For example, for room-level classification purposes, it is required that the signal observations related to each room exhibit significant differences in their RSSI values. However, it is difficult to verify and visualize that separability since radio maps are constituted by multi-dimensional observations whose dimension is directly related to the number of access points or monitors being employed for localization purposes. In this paper, we propose a refinement cycle for passive indoor positioning systems, which is based on dimensionality reduction techniques, to evaluate the quality of a radio map. By means of these techniques and our own data representation, we have defined two different visualization methods to obtain graphical information about the quality of a particular radio map in terms of overlapping areas and outliers. That information will be useful to determine whether new monitors are required or some existing ones should be moved. We have performed an exhaustive experimental analysis based on a variety of different scenarios, some deployed by our own research group and others corresponding to a well-known existing dataset widely analyzed by the community, in order to validate our proposal. As we will show, among the different combinations of data representation methods and dimensionality reduction techniques that we discuss, we have found that there are some specific configurations that are more useful in order to perform the refinement process. Full article
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Open AccessArticle Modeling Infrared Signal Reflections to Characterize Indoor Multipath Propagation
Sensors 2017, 17(4), 847; doi:10.3390/s17040847
Received: 27 February 2017 / Revised: 3 April 2017 / Accepted: 7 April 2017 / Published: 13 April 2017
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Abstract
In this paper, we propose a model to characterize Infrared (IR) signal reflections on any kind of surface material, together with a simplified procedure to compute the model parameters. The model works within the framework of Local Positioning Systems (LPS) based on IR
[...] Read more.
In this paper, we propose a model to characterize Infrared (IR) signal reflections on any kind of surface material, together with a simplified procedure to compute the model parameters. The model works within the framework of Local Positioning Systems (LPS) based on IR signals (IR-LPS) to evaluate the behavior of transmitted signal Multipaths (MP), which are the main cause of error in IR-LPS, and makes several contributions to mitigation methods. Current methods are based on physics, optics, geometry and empirical methods, but these do not meet our requirements because of the need to apply several different restrictions and employ complex tools. We propose a simplified model based on only two reflection components, together with a method for determining the model parameters based on 12 empirical measurements that are easily performed in the real environment where the IR-LPS is being applied. Our experimental results show that the model provides a comprehensive solution to the real behavior of IR MP, yielding small errors when comparing real and modeled data (the mean error ranges from 1% to 4% depending on the environment surface materials). Other state-of-the-art methods yielded mean errors ranging from 15% to 40% in test measurements. Full article
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Open AccessArticle Hand-Based Gesture Recognition for Vehicular Applications Using IR-UWB Radar
Sensors 2017, 17(4), 833; doi:10.3390/s17040833
Received: 26 February 2017 / Revised: 28 March 2017 / Accepted: 6 April 2017 / Published: 11 April 2017
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Abstract
Modern cars continue to offer more and more functionalities due to which they need a growing number of commands. As the driver tries to monitor the road and the graphic user interface simultaneously, his/her overall efficiency is reduced. In order to reduce the
[...] Read more.
Modern cars continue to offer more and more functionalities due to which they need a growing number of commands. As the driver tries to monitor the road and the graphic user interface simultaneously, his/her overall efficiency is reduced. In order to reduce the visual attention necessary for monitoring, a gesture-based user interface is very important. In this paper, gesture recognition for a vehicle through impulse radio ultra-wideband (IR-UWB) radar is discussed. The gestures can be used to control different electronic devices inside a vehicle. The gestures are based on human hand and finger motion. We have implemented a real-time version using only one radar sensor. Studies on gesture recognition using IR-UWB radar have rarely been carried out, and some studies are merely simple methods using the magnitude of the reflected signal or those whose performance deteriorates largely due to changes in distance or direction. In this study, we propose a new hand-based gesture recognition algorithm that works robustly against changes in distance or direction while responding only to defined gestures by ignoring meaningless motions. We used three independent features, i.e., variance of the probability density function (pdf) of the magnitude histogram, time of arrival (TOA) variation and the frequency of the reflected signal, to classify the gestures. A data fitting method is included to differentiate between gesture signals and unintended hand or body motions. We have used the clustering technique for the classification of the gestures. Moreover, the distance information is used as an additional input parameter to the clustering algorithm, such that the recognition technique will not be vulnerable to distance change. The hand-based gesture recognition proposed in this paper would be a key technology of future automobile user interfaces. Full article
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Open AccessArticle A Novel Robust Trilateration Method Applied to Ultra-Wide Bandwidth Location Systems
Sensors 2017, 17(4), 795; doi:10.3390/s17040795
Received: 14 February 2017 / Revised: 25 March 2017 / Accepted: 31 March 2017 / Published: 7 April 2017
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Abstract
Due to the non-line-of-sight (NLOS) and multipath fading channel (MPF) of the wireless networks, the non-existence of the intersection point often occurs in the range-based localization methods, e.g., the centroid-based trilateration method. To alleviate the problem, a confidence-based intersection method which expands the
[...] Read more.
Due to the non-line-of-sight (NLOS) and multipath fading channel (MPF) of the wireless networks, the non-existence of the intersection point often occurs in the range-based localization methods, e.g., the centroid-based trilateration method. To alleviate the problem, a confidence-based intersection method which expands the range of the circle within a certain confidence interval is proposed. In the method, the confidence interval is estimated based on the Cramér–Rao lower bound of the time of flight (TOF) measurement. Furthermore, an intersection determination method is proposed to select the intersection point with higher confidence level. The simulation and experimental results show the superiority of the proposed method in localization accuracy and robustness to noise compared to the conventional trilateration method, e.g., the centroid-based and least squares based trilateration methods. Full article
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Open AccessArticle An Improved Approach for RSSI-Based only Calibration-Free Real-Time Indoor Localization on IEEE 802.11 and 802.15.4 Wireless Networks
Sensors 2017, 17(4), 717; doi:10.3390/s17040717
Received: 31 January 2017 / Revised: 15 March 2017 / Accepted: 25 March 2017 / Published: 29 March 2017
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Abstract
Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where
[...] Read more.
Assuming a reliable and responsive spatial contextualization service is a must-have in IEEE 802.11 and 802.15.4 wireless networks, a suitable approach consists of the implementation of localization capabilities, as an additional application layer to the communication protocol stack. Considering the applicative scenario where satellite-based positioning applications are denied, such as indoor environments, and excluding data packet arrivals time measurements due to lack of time resolution, received signal strength indicator (RSSI) measurements, obtained according to IEEE 802.11 and 802.15.4 data access technologies, are the unique data sources suitable for indoor geo-referencing using COTS devices. In the existing literature, many RSSI based localization systems are introduced and experimentally validated, nevertheless they require periodic calibrations and significant information fusion from different sensors that dramatically decrease overall systems reliability and their effective availability. This motivates the work presented in this paper, which introduces an approach for an RSSI-based calibration-free and real-time indoor localization. While switched-beam array-based hardware (compliant with IEEE 802.15.4 router functionality) has already been presented by the author, the focus of this paper is the creation of an algorithmic layer for use with the pre-existing hardware capable to enable full localization and data contextualization over a standard 802.15.4 wireless sensor network using only RSSI information without the need of lengthy offline calibration phase. System validation reports the localization results in a typical indoor site, where the system has shown high accuracy, leading to a sub-metrical overall mean error and an almost 100% site coverage within 1 m localization error. Full article
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Open AccessArticle Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring
Sensors 2017, 17(2), 351; doi:10.3390/s17020351
Received: 30 November 2016 / Revised: 21 January 2017 / Accepted: 25 January 2017 / Published: 11 February 2017
PDF Full-text (4757 KB) | HTML Full-text | XML Full-text
Abstract
The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New
[...] Read more.
The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New approaches seek the indirect monitoring through monitoring activities of daily living (ADLs), which proves to be a suitable solution. ADL monitoring systems use many heterogeneous sensors, are less intrusive, and are less expensive than BSN, however, the deployment and maintenance of wireless sensor networks (WSN) prevent them from a widespread acceptance. In this work, a novel technique to monitor the human activity, based on non-intrusive load monitoring (NILM), is presented. The proposal uses only smart meter data, which leads to minimum intrusiveness and a potential massive deployment at minimal cost. This could be the key to develop sustainable healthcare models for smart homes, capable of complying with the elderly people’ demands. This study also uses the Dempster-Shafer theory to provide a daily score of normality with regard to the regular behavior. This approach has been evaluated using real datasets and, additionally, a benchmarking against a Gaussian mixture model approach is presented. Full article
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Open AccessArticle Reliable Identification of Vehicle-Boarding Actions Based on Fuzzy Inference Syste
Sensors 2017, 17(2), 333; doi:10.3390/s17020333
Received: 31 December 2016 / Revised: 29 January 2017 / Accepted: 6 February 2017 / Published: 9 February 2017
Cited by 1 | PDF Full-text (2118 KB) | HTML Full-text | XML Full-text
Abstract
Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a
[...] Read more.
Existing smartphone-based solutions to prevent distracted driving suffer from inadequate system designs that only recognize simple and clean vehicle-boarding actions, thereby failing to meet the required level of accuracy in real-life environments. In this paper, exploiting unique sensory features consistently monitored from a broad range of complicated vehicle-boarding actions, we propose a reliable and accurate system based on fuzzy inference to classify the sides of vehicle entrancebyleveragingbuilt-insmartphonesensorsonly. Theresultsofourcomprehensiveevaluation on three vehicle types with four participants demonstrate that the proposed system achieves 91.1%∼94.0% accuracy, outperforming other methods by 26.9%∼38.4% and maintains at least 87.8 %accuracy regardless of smartphone positions and vehicle types. Full article
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