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Castaño F, Beruvides G, Haber RE, Artuñedo A. Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System. SENSORS 2017; 17:s17092109. [PMID: 28906450 PMCID: PMC5620580 DOI: 10.3390/s17092109] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 09/08/2017] [Accepted: 09/12/2017] [Indexed: 11/16/2022]
Abstract
Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors' knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.
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Affiliation(s)
- Fernando Castaño
- Centre for Automation and Robotics, Technical University of Madrid-Spanish National Research Council (UPM-CSIC), Ctra. Campo Real Km. 0.2, Arganda del Rey 28500, Spain.
| | - Gerardo Beruvides
- Centre for Automation and Robotics, Technical University of Madrid-Spanish National Research Council (UPM-CSIC), Ctra. Campo Real Km. 0.2, Arganda del Rey 28500, Spain.
| | - Rodolfo E Haber
- Centre for Automation and Robotics, Technical University of Madrid-Spanish National Research Council (UPM-CSIC), Ctra. Campo Real Km. 0.2, Arganda del Rey 28500, Spain.
| | - Antonio Artuñedo
- Centre for Automation and Robotics, Technical University of Madrid-Spanish National Research Council (UPM-CSIC), Ctra. Campo Real Km. 0.2, Arganda del Rey 28500, Spain.
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Quero JM, Ruiz Lozano MD, Castañeda García JA, Rodriguez Molina MA, Frias Jamilena DM. A Dynamic Fuzzy Temporal Clustering for Imprecise Location Streams. INT J UNCERTAIN FUZZ 2017. [DOI: 10.1142/s0218488517500179] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The clustering has provided data analysis in many contexts of Computer Science. It is widely applied in Ambient Intelligence and Ubiquitous Computing for information processing, with geolocation data prominently. In this paper, we introduce a dynamic fuzzy temporal clustering algorithm (DFTC) to detect stays of users in urban environments based on locations from imprecise sensors. Our approach includes fuzzy evaluation of temporal and probabilistic data providing analysis in real time. As results, we have developed a mobile application which integrates the DFTC and detects satisfactorily user stays related to urban commerces from a real environment.
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Affiliation(s)
- J. Medina Quero
- Department of Computer Science and Artificial Intelligence, University of Granada c/. Daniel Saucedo Aranda, 18071 Granada, Spain
| | - M. D. Ruiz Lozano
- Department of Computer Science and Artificial Intelligence, University of Granada c/. Daniel Saucedo Aranda, 18071 Granada, Spain
| | - J. A. Castañeda García
- Department of Marketing and Market Research, University Campus of Cartuja, C.P. 18071 Granada, Spain
| | - M. A. Rodriguez Molina
- Department of Marketing and Market Research, University Campus of Cartuja, C.P. 18071 Granada, Spain
| | - D. M. Frias Jamilena
- Department of Marketing and Market Research, University Campus of Cartuja, C.P. 18071 Granada, Spain
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Palermo E, Rossi S, Patanè F, Cappa P. Experimental evaluation of indoor magnetic distortion effects on gait analysis performed with wearable inertial sensors. Physiol Meas 2014; 35:399-415. [PMID: 24499774 DOI: 10.1088/0967-3334/35/3/399] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Magnetic inertial measurement unit systems (MIMU) offer the potential to perform joint kinematics evaluation as an alternative to optoelectronic systems (OS). Several studies have reported the effect of indoor magnetic field disturbances on the MIMU's heading output, even though the overall effect on the evaluation of lower limb joint kinematics is not yet fully explored. The aim of the study is to assess the influence of indoor magnetic field distortion on gait analysis trials conducted with a commercial MIMU system. A healthy adult performed gait analysis sessions both indoors and outdoors. Data collected indoors were post-processed with and without a heading correction methodology performed with OS at the start of the gait trial. The performance of the MIMU system is characterized in terms of indices, based on the mean value of lower limb joint angles and the associated ROM, quantifying the system repeatability. We find that the effects of magnetic field distortion, such as the one we experienced in our lab, were limited to the transverse plane of each joint and to the frontal plane of the ankle. Sagittal plane values, instead, showed sufficient repeatability moving from outdoors to indoors. Our findings provide indications to clinicians on MIMU performance in the measurement of lower limb kinematics.
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Affiliation(s)
- E Palermo
- Department of Mechanical and Aerospace Engineering, 'Sapienza' University of Rome, Via Eudossiana 18, I-00184 Roma, Italy. Movement Analysis and Robotics Laboratory (MARLab), Neurorehabilitation Division, IRCCS Children's Hospital 'Bambino Gesù', Via Torre di Palidoro, I-00050 Passoscuro (Fiumicino) Roma, Italy
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Dhital A, Bancroft JB, Lachapelle G. A new approach for improving reliability of personal navigation devices under harsh GNSS signal conditions. SENSORS 2013; 13:15221-41. [PMID: 24212120 PMCID: PMC3871101 DOI: 10.3390/s131115221] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2013] [Revised: 10/23/2013] [Accepted: 10/30/2013] [Indexed: 11/16/2022]
Abstract
In natural and urban canyon environments, Global Navigation Satellite System (GNSS) signals suffer from various challenges such as signal multipath, limited or lack of signal availability and poor geometry. Inertial sensors are often employed to improve the solution continuity under poor GNSS signal quality and availability conditions. Various fault detection schemes have been proposed in the literature to detect and remove biased GNSS measurements to obtain a more reliable navigation solution. However, many of these methods are found to be sub-optimal and often lead to unavailability of reliability measures, mostly because of the improper characterization of the measurement errors. A robust filtering architecture is thus proposed which assumes a heavy-tailed distribution for the measurement errors. Moreover, the proposed filter is capable of adapting to the changing GNSS signal conditions such as when moving from open sky conditions to deep canyons. Results obtained by processing data collected in various GNSS challenged environments show that the proposed scheme provides a robust navigation solution without having to excessively reject usable measurements. The tests reported herein show improvements of nearly 15% and 80% for position accuracy and reliability, respectively, when applying the above approach.
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Affiliation(s)
- Anup Dhital
- Department of Geomatics Engineering, Schulich School of Engineering, The University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada.
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Placer M, Kovačič S. Enhancing indoor inertial pedestrian navigation using a shoe-worn marker. SENSORS 2013; 13:9836-59. [PMID: 23917258 PMCID: PMC3812582 DOI: 10.3390/s130809836] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2013] [Revised: 07/18/2013] [Accepted: 07/25/2013] [Indexed: 11/16/2022]
Abstract
We propose a novel hybrid inertial sensors-based indoor pedestrian dead reckoning system, aided by computer vision-derived position measurements. In contrast to prior vision-based or vision-aided solutions, where environmental markers were used-either deployed in known positions or extracted directly from it-we use a shoe-fixed marker, which serves as positional reference to an opposite shoe-mounted camera during foot swing, making our system self-contained. Position measurements can be therefore more reliably fed to a complementary unscented Kalman filter, enhancing the accuracy of the estimated travelled path for 78%, compared to using solely zero velocities as pseudo-measurements.
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Affiliation(s)
- Mitja Placer
- Harpha Sea, Čevljarska Ulica 8, Koper 6000, Slovenia
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +386-5-663-89-24; Fax: +386-5-663-89-29
| | - Stanislav Kovačič
- Fakulteta za Elektrotehniko, Univerza v Ljubljani, Tržaška 25, Ljubljana 1000, Slovenia; E-Mail:
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Susi M, Renaudin V, Lachapelle G. Motion mode recognition and step detection algorithms for mobile phone users. SENSORS (BASEL, SWITZERLAND) 2013; 13:1539-62. [PMID: 23348038 PMCID: PMC3649428 DOI: 10.3390/s130201539] [Citation(s) in RCA: 186] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2012] [Revised: 01/18/2013] [Accepted: 01/22/2013] [Indexed: 11/16/2022]
Abstract
Microelectromechanical Systems (MEMS) technology is playing a key role in the design of the new generation of smartphones. Thanks to their reduced size, reduced power consumption, MEMS sensors can be embedded in above mobile devices for increasing their functionalities. However, MEMS cannot allow accurate autonomous location without external updates, e.g., from GPS signals, since their signals are degraded by various errors. When these sensors are fixed on the user's foot, the stance phases of the foot can easily be determined and periodic Zero velocity UPdaTes (ZUPTs) are performed to bound the position error. When the sensor is in the hand, the situation becomes much more complex. First of all, the hand motion can be decoupled from the general motion of the user. Second, the characteristics of the inertial signals can differ depending on the carrying modes. Therefore, algorithms for characterizing the gait cycle of a pedestrian using a handheld device have been developed. A classifier able to detect motion modes typical for mobile phone users has been designed and implemented. According to the detected motion mode, adaptive step detection algorithms are applied. Success of the step detection process is found to be higher than 97% in all motion modes.
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Affiliation(s)
- Melania Susi
- PLAN Group, Schulich School of Engineering, The University of Calgary, Calgary, AB, Canada.
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Martí ED, Martín D, García J, de la Escalera A, Molina JM, Armingol JM. Context-aided sensor fusion for enhanced urban navigation. SENSORS 2012; 12:16802-37. [PMID: 23223080 PMCID: PMC3571812 DOI: 10.3390/s121216802] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Revised: 11/30/2012] [Accepted: 12/03/2012] [Indexed: 11/24/2022]
Abstract
The deployment of Intelligent Vehicles in urban environments requires reliable estimation of positioning for urban navigation. The inherent complexity of this kind of environments fosters the development of novel systems which should provide reliable and precise solutions to the vehicle. This article details an advanced GNSS/IMU fusion system based on a context-aided Unscented Kalman filter for navigation in urban conditions. The constrained non-linear filter is here conditioned by a contextual knowledge module which reasons about sensor quality and driving context in order to adapt it to the situation, while at the same time it carries out a continuous estimation and correction of INS drift errors. An exhaustive analysis has been carried out with available data in order to characterize the behavior of available sensors and take it into account in the developed solution. The performance is then analyzed with an extensive dataset containing representative situations. The proposed solution suits the use of fusion algorithms for deploying Intelligent Transport Systems in urban environments.
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Affiliation(s)
- Enrique David Martí
- Applied Artificial Intelligence Group, Universidad Carlos III de Madrid, Avda de la Universidad Carlos III 22, 28270 Colmenarejo, Spain; E-Mails: (J.G.); (J.M.M.)
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +34-91-856-1338
| | - David Martín
- Intelligent Systems Lab, Universidad Carlos III de Madrid, Avda de la Universidad 30, 28911 Leganes, Spain; E-Mails: (D.M.); (A.E.); (J.M.A.)
| | - Jesús García
- Applied Artificial Intelligence Group, Universidad Carlos III de Madrid, Avda de la Universidad Carlos III 22, 28270 Colmenarejo, Spain; E-Mails: (J.G.); (J.M.M.)
| | - Arturo de la Escalera
- Intelligent Systems Lab, Universidad Carlos III de Madrid, Avda de la Universidad 30, 28911 Leganes, Spain; E-Mails: (D.M.); (A.E.); (J.M.A.)
| | - José Manuel Molina
- Applied Artificial Intelligence Group, Universidad Carlos III de Madrid, Avda de la Universidad Carlos III 22, 28270 Colmenarejo, Spain; E-Mails: (J.G.); (J.M.M.)
| | - José María Armingol
- Intelligent Systems Lab, Universidad Carlos III de Madrid, Avda de la Universidad 30, 28911 Leganes, Spain; E-Mails: (D.M.); (A.E.); (J.M.A.)
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Hsu CC, Chen HC, Su YN, Huang KK, Huang YM. Developing a reading concentration monitoring system by applying an artificial bee colony algorithm to e-books in an intelligent classroom. SENSORS 2012. [PMID: 23202042 PMCID: PMC3545613 DOI: 10.3390/s121014158] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.
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Affiliation(s)
- Chia-Cheng Hsu
- Department of Engineering Science, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan; E-Mails: (C.-C.H.); (H.-C.C.); (Y.-N.S.)
| | - Hsin-Chin Chen
- Department of Engineering Science, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan; E-Mails: (C.-C.H.); (H.-C.C.); (Y.-N.S.)
| | - Yen-Ning Su
- Department of Engineering Science, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan; E-Mails: (C.-C.H.); (H.-C.C.); (Y.-N.S.)
| | - Kuo-Kuang Huang
- Department of Information Management, National Penghu University of Science and Technology, No.300, Liuhe Road, Magong City, Penghu County 880, Taiwan; E-Mail:
| | - Yueh-Min Huang
- Department of Engineering Science, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan; E-Mails: (C.-C.H.); (H.-C.C.); (Y.-N.S.)
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +886-6-275-7575 (ext. 63336)
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Renaudin V, Susi M, Lachapelle G. Step length estimation using handheld inertial sensors. SENSORS (BASEL, SWITZERLAND) 2012; 12:8507-25. [PMID: 23012503 PMCID: PMC3444061 DOI: 10.3390/s120708507] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2012] [Revised: 06/12/2012] [Accepted: 06/13/2012] [Indexed: 11/17/2022]
Abstract
In this paper a novel step length model using a handheld Micro Electrical Mechanical System (MEMS) is presented. It combines the user's step frequency and height with a set of three parameters for estimating step length. The model has been developed and trained using 12 different subjects: six men and six women. For reliable estimation of the step frequency with a handheld device, the frequency content of the handheld sensor's signal is extracted by applying the Short Time Fourier Transform (STFT) independently from the step detection process. The relationship between step and hand frequencies is analyzed for different hand's motions and sensor carrying modes. For this purpose, the frequency content of synchronized signals collected with two sensors placed in the hand and on the foot of a pedestrian has been extracted. Performance of the proposed step length model is assessed with several field tests involving 10 test subjects different from the above 12. The percentages of error over the travelled distance using universal parameters and a set of parameters calibrated for each subject are compared. The fitted solutions show an error between 2.5 and 5% of the travelled distance, which is comparable with that achieved by models proposed in the literature for body fixed sensors only.
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Affiliation(s)
- Valérie Renaudin
- PLAN Group, Schulich School of Engineering, The University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada; E-Mails: (M.S.); (G.L.)
| | - Melania Susi
- PLAN Group, Schulich School of Engineering, The University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada; E-Mails: (M.S.); (G.L.)
| | - Gérard Lachapelle
- PLAN Group, Schulich School of Engineering, The University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada; E-Mails: (M.S.); (G.L.)
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