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Wei R, Xu H, Yang M, Yu X, Xiao Z, Yan B. Real-Time Pedestrian Tracking Terminal Based on Adaptive Zero Velocity Update. SENSORS 2021; 21:s21113808. [PMID: 34072810 PMCID: PMC8198276 DOI: 10.3390/s21113808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/14/2021] [Accepted: 05/27/2021] [Indexed: 11/16/2022]
Abstract
In the field of pedestrian dead reckoning (PDR), the zero velocity update (ZUPT) method with an inertial measurement unit (IMU) is a mature technology to calibrate dead reckoning. However, due to the complex walking modes of different individuals, it is essential and challenging to determine the ZUPT conditions, which has a direct and significant influence on the tracking accuracy. In this research, we adopted an adaptive zero velocity update (AZUPT) method based on convolution neural networks to classify the ZUPT conditions. The AZUPT model was robust regardless of the different motion types of various individuals. AZUPT was then implemented on the Zynq-7000 SoC platform to work in real time to validate its computational efficiency and performance superiority. Extensive real-world experiments were conducted by 60 different individuals in three different scenarios. It was demonstrated that the proposed system could work equally well in different environments, making it portable for PDR to be widely performed in various real-world situations.
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Okkalidis N, Camilleri KP, Gatt A, Bugeja MK, Falzon O. A review of foot pose and trajectory estimation methods using inertial and auxiliary sensors for kinematic gait analysis. BIOMED ENG-BIOMED TE 2020; 65:/j/bmte.ahead-of-print/bmt-2019-0163/bmt-2019-0163.xml. [PMID: 32589591 DOI: 10.1515/bmt-2019-0163] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 03/09/2020] [Indexed: 11/15/2022]
Abstract
The use of foot mounted inertial and other auxiliary sensors for kinematic gait analysis has been extensively investigated during the last years. Although, these sensors still yield less accurate results than those obtained employing optical motion capture systems, the miniaturization and their low cost have allowed the estimation of kinematic spatiotemporal parameters in laboratory conditions and real life scenarios. The aim of this work was to present a comprehensive approach of this scientific area through a systematic literature research, breaking down the state-of-the-art methods into three main parts: (1) zero velocity interval detection techniques; (2) assumptions and sensors' utilization; (3) foot pose and trajectory estimation methods. Published articles from 1995 until December of 2018 were searched in the PubMed, IEEE Xplore and Google Scholar databases. The research was focused on two categories: (a) zero velocity interval detection methods; and (b) foot pose and trajectory estimation methods. The employed assumptions and the potential use of the sensors have been identified from the retrieved articles. Technical characteristics, categorized methodologies, application conditions, advantages and disadvantages have been provided, while, for the first time, assumptions and sensors' utilization have been identified, categorized and are presented in this review. Considerable progress has been achieved in gait parameters estimation on constrained laboratory environments taking into account assumptions such as a person walking on a flat floor. On the contrary, methods that rely on less constraining assumptions, and are thus applicable in daily life, led to less accurate results. Rule based methods have been mainly used for the detection of the zero velocity intervals, while more complex techniques have been proposed, which may lead to more accurate gait parameters. The review process has shown that presently the best-performing methods for gait parameter estimation make use of inertial sensors combined with auxiliary sensors such as ultrasonic sensors, proximity sensors and cameras. However, the experimental evaluation protocol was much more thorough, when single inertial sensors were used. Finally, it has been highlighted that the accuracy of setups using auxiliary sensors may further be improved by collecting measurements during the whole foot movement and not only partially as is currently the practice. This review has identified the need for research and development of methods and setups that allow for the robust estimation of kinematic gait parameters in unconstrained environments and under various gait profiles.
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Affiliation(s)
| | - Kenneth P Camilleri
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta
- Department of Systems and Control Engineering, University of Malta, Msida, Malta
| | - Alfred Gatt
- Department of Podiatry, Faculty of Health Sciences, University of Malta, Msida, Malta
| | - Marvin K Bugeja
- Department of Systems and Control Engineering, University of Malta, Msida, Malta
| | - Owen Falzon
- Centre for Biomedical Cybernetics, University of Malta, Msida, Malta
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Spline Function Simulation Data Generation for Walking Motion Using Foot-Mounted Inertial Sensors. ELECTRONICS 2018. [DOI: 10.3390/electronics8010018] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This paper investigates the generation of simulation data for motion estimation using inertial sensors. The smoothing algorithm with waypoint-based map matching is proposed using foot-mounted inertial sensors to estimate position and attitude. The simulation data are generated using spline functions, where the estimated position and attitude are used as control points. The attitude is represented using B-spline quaternion and the position is represented by eighth-order algebraic splines. The simulation data can be generated using inertial sensors (accelerometer and gyroscope) without using any additional sensors. Through indoor experiments, two scenarios were examined include 2D walking path (rectangular) and 3D walking path (corridor and stairs) for simulation data generation. The proposed simulation data is used to evaluate the estimation performance with different parameters such as different noise levels and sampling periods.
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Li F, Liu G, Liu J, Chen X, Ma X. 3D Tracking via Shoe Sensing. SENSORS 2016; 16:s16111809. [PMID: 27801839 PMCID: PMC5134468 DOI: 10.3390/s16111809] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Revised: 10/15/2016] [Accepted: 10/20/2016] [Indexed: 11/19/2022]
Abstract
Most location-based services are based on a global positioning system (GPS), which only works well in outdoor environments. Compared to outdoor environments, indoor localization has created more buzz in recent years as people spent most of their time indoors working at offices and shopping at malls, etc. Existing solutions mainly rely on inertial sensors (i.e., accelerometer and gyroscope) embedded in mobile devices, which are usually not accurate enough to be useful due to the mobile devices’ random movements while people are walking. In this paper, we propose the use of shoe sensing (i.e., sensors attached to shoes) to achieve 3D indoor positioning. Specifically, a short-time energy-based approach is used to extract the gait pattern. Moreover, in order to improve the accuracy of vertical distance estimation while the person is climbing upstairs, a state classification is designed to distinguish the walking status including plane motion (i.e., normal walking and jogging horizontally), walking upstairs, and walking downstairs. Furthermore, we also provide a mechanism to reduce the vertical distance accumulation error. Experimental results show that we can achieve nearly 100% accuracy when extracting gait patterns from walking/jogging with a low-cost shoe sensor, and can also achieve 3D indoor real-time positioning with high accuracy.
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Affiliation(s)
- Fangmin Li
- Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China.
- Key Laboratory of Fiber Optical Sensing Technology and Information Processing, Ministry of Education, School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
| | - Guo Liu
- Key Laboratory of Fiber Optical Sensing Technology and Information Processing, Ministry of Education, School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
| | - Jian Liu
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA.
| | - Xiaochuang Chen
- Key Laboratory of Fiber Optical Sensing Technology and Information Processing, Ministry of Education, School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
| | - Xiaolin Ma
- Key Laboratory of Fiber Optical Sensing Technology and Information Processing, Ministry of Education, School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
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Ren M, Pan K, Liu Y, Guo H, Zhang X, Wang P. A Novel Pedestrian Navigation Algorithm for a Foot-Mounted Inertial-Sensor-Based System. SENSORS 2016; 16:s16010139. [PMID: 26805848 PMCID: PMC4732172 DOI: 10.3390/s16010139] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Revised: 01/07/2016] [Accepted: 01/18/2016] [Indexed: 11/22/2022]
Abstract
This paper proposes a novel zero velocity update (ZUPT) method for a foot-mounted pedestrian navigation system (PNS). First, the error model of the PNS is developed and a Kalman filter is built based on the error model. Second, a novel zero velocity detection algorithm based on the variations in speed over a gait cycle is proposed. A finite state machine including three states is employed to model a gait cycle. The state transition conditions are determined based on speed using a sliding window. Third, the ZUPT software flow is illustrated and described. Finally, the performances of the proposed method and other methods are examined and compared experimentally. The experimental results show that the mean relative accuracy of the proposed method is 0.89% under various motion modes.
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Affiliation(s)
- Mingrong Ren
- School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China.
- Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China.
| | - Kai Pan
- School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China.
- Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing 100124, China.
| | - Yanhong Liu
- School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China.
| | - Hongyu Guo
- School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China.
| | - Xiaodong Zhang
- School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China.
| | - Pu Wang
- School of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China.
- Beijing Laboratory for Urban Mass Transit, Beijing 100124, China.
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Pedestrian Navigation Using Foot-Mounted Inertial Sensor and LIDAR. SENSORS 2016; 16:s16010120. [PMID: 26797619 PMCID: PMC4732153 DOI: 10.3390/s16010120] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2015] [Revised: 01/14/2016] [Accepted: 01/15/2016] [Indexed: 11/16/2022]
Abstract
Foot-mounted inertial sensors can be used for indoor pedestrian navigation. In this paper, to improve the accuracy of pedestrian location, we propose a method using a distance sensor (LIDAR) in addition to an inertial measurement unit (IMU). The distance sensor is a time of flight range finder with 30 m measurement range (at 33.33 Hz). Using a distance sensor, walls on corridors are automatically detected. The detected walls are used to correct the heading of the pedestrian path. Through experiments, it is shown that the accuracy of the heading is significantly improved using the proposed algorithm. Furthermore, the system is shown to work robustly in indoor environments with many doors and passing people.
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He C, Kazanzides P, Sen HT, Kim S, Liu Y. An Inertial and Optical Sensor Fusion Approach for Six Degree-of-Freedom Pose Estimation. SENSORS 2015; 15:16448-65. [PMID: 26184191 PMCID: PMC4541887 DOI: 10.3390/s150716448] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 06/21/2015] [Accepted: 06/24/2015] [Indexed: 11/17/2022]
Abstract
Optical tracking provides relatively high accuracy over a large workspace but requires line-of-sight between the camera and the markers, which may be difficult to maintain in actual applications. In contrast, inertial sensing does not require line-of-sight but is subject to drift, which may cause large cumulative errors, especially during the measurement of position. To handle cases where some or all of the markers are occluded, this paper proposes an inertial and optical sensor fusion approach in which the bias of the inertial sensors is estimated when the optical tracker provides full six degree-of-freedom (6-DOF) pose information. As long as the position of at least one marker can be tracked by the optical system, the 3-DOF position can be combined with the orientation estimated from the inertial measurements to recover the full 6-DOF pose information. When all the markers are occluded, the position tracking relies on the inertial sensors that are bias-corrected by the optical tracking system. Experiments are performed with an augmented reality head-mounted display (ARHMD) that integrates an optical tracking system (OTS) and inertial measurement unit (IMU). Experimental results show that under partial occlusion conditions, the root mean square errors (RMSE) of orientation and position are 0.04° and 0.134 mm, and under total occlusion conditions for 1 s, the orientation and position RMSE are 0.022° and 0.22 mm, respectively. Thus, the proposed sensor fusion approach can provide reliable 6-DOF pose under long-term partial occlusion and short-term total occlusion conditions.
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Affiliation(s)
- Changyu He
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China.
| | - Peter Kazanzides
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Hasan Tutkun Sen
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Sungmin Kim
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Yue Liu
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China.
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