On Four-Condition Zero-Speed Detection Algorithm for Inertial Pedestrian Navigation

2021 ◽  
pp. 3359-3369
Author(s):  
Dai Shaowu ◽  
Zheng Weiwei ◽  
Dai Hongde
Keyword(s):  
Detection Algorithm ◽  
Pedestrian Navigation

scholarly journals An Indoor Localization Method Based on the Combination of Indoor Map Information and Inertial Navigation with Cascade Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yushuai Zhang ◽  
Jianxin Guo ◽  
Feng Wang ◽  
Rui Zhu ◽  
Liping Wang
Keyword(s):  
Indoor Localization ◽  
Inertial Navigation ◽  
Detection Algorithm ◽  
Position Estimation ◽  
Position Error ◽  
Indoor Navigation ◽  
Bayesian Filter ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Average Position

The specific objective of this study is to propose a low-cost indoor navigation framework with nonbasic equipment by combining inertial sensors and indoor map messages. The proposed pedestrian navigation framework consists of a lower filter and an upper filter. In the lower filter which is designed based on the Kalman filter, the adaptive zero velocity detection algorithm is used to detect the zero velocity interval at different motion speeds, and then, zero velocity update is applied to rectify the inertial navigation solutions’ errors. In the upper filter which is designed based on the nonrecursive Bayesian filter, the map matching method with nonrecursive Bayesian filter is adopted to fuse the map prior information and the lower filter estimation results to correct the errors of navigation. The position estimation presented in this study achieves an average position error of 0.53 m compared to the ZUPT-aided inertial navigation system (INS) method under different motion states. The proposed pedestrian navigation algorithm achieves an average position error of 0.54 m as compared to the ZUPT-aided INS method among the different tested distances. The proposed framework simplifies the indoor positioning system under multiple motion speed conditions by ensuring the accuracy and stability property. The effectiveness and accuracy of the proposed framework are experimentally verified in various real-world scenarios.

scholarly journals A Zero Velocity Detection Algorithm Using Inertial Sensors for Pedestrian Navigation Systems

Sensors ◽  
2010 ◽  
Vol 10 (10) ◽  
pp. 9163-9178 ◽  
Author(s):  
Sang Kyeong Park ◽  
Young Soo Suh
Keyword(s):  
Inertial Sensors ◽  
Detection Algorithm ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Zero Velocity

scholarly journals A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors

Sensors ◽  
2016 ◽  
Vol 16 (10) ◽  
pp. 1578 ◽  
Author(s):  
Xiaochun Tian ◽  
Jiabin Chen ◽  
Yongqiang Han ◽  
Jianyu Shang ◽  
Nan Li
Keyword(s):  
Navigation System ◽  
Inertial Sensors ◽  
Detection Algorithm ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Velocity Interval

An Improved Bayesian Zero-Velocity Detection Algorithm for Pedestrian Navigation Based on MIMU

2021 ◽  
Author(s):  
XiaoYu Zhang ◽  
Shaowu Dai ◽  
Hongde Dai ◽  
WenJie Quau ◽  
Yang Zhao
Keyword(s):  
Detection Algorithm ◽  
Pedestrian Navigation ◽  
Zero Velocity

scholarly journals An Adaptive Zero Velocity Detection Algorithm Based on Multi-Sensor Fusion for a Pedestrian Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3261 ◽  
Author(s):  
Ming Ma ◽  
Qian Song ◽  
Yang Gu ◽  
Yanghuan Li ◽  
Zhimin Zhou
Keyword(s):  
Sensor Fusion ◽  
Navigation System ◽  
Detection Algorithm ◽  
Stair Climbing ◽  
Detection Methods ◽  
Recognition Method ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Motion Modes ◽  
Detection Approach

The zero velocity update (ZUPT) algorithm is an effective way to suppress the error growth for a foot-mounted pedestrian navigation system. To make ZUPT work properly, it is necessary to detect zero velocity intervals correctly. Existing zero velocity detection methods cannot provide good performance at high gait speeds or stair climbing. An adaptive zero velocity detection approach based on multi-sensor fusion is proposed in this paper. The measurements of an accelerometer, gyroscope and pressure sensor were employed to construct a zero-velocity detector. Then, the adaptive threshold was proposed to improve the accuracy of the detector under various motion modes. In addition, to eliminate the height drift, a stairs recognition method was developed to distinguish staircase movement from level walking. Detection performance was examined with experimental data collected at varying motion modes in real scenarios. The experimental results indicate that the proposed method can correctly detect zero velocity intervals under various motion modes.

A Zero-Velocity Detection Algorithm for Pedestrian Navigation Based on LSTM

2021 ◽  
Author(s):  
Langping An ◽  
Xianfei Pan ◽  
Mang Wang ◽  
Ze Chen ◽  
Zheming Tu ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Pedestrian Navigation ◽  
Zero Velocity

Step Detection Algorithm Using Fast Fourier Transformation

2014 ◽  
Vol 1049-1050 ◽  
pp. 1218-1221
Author(s):  
He Zhang ◽  
Rui Peng ◽  
Xiao Dong Zhao
Keyword(s):  
Fourier Transformation ◽  
Dead Reckoning ◽  
Detection Algorithm ◽  
Fast Fourier Transformation ◽  
Step Detection ◽  
Pedestrian Navigation ◽  
Improve Accuracy ◽  
Basic Portion ◽  
Task Step ◽  
Pedestrian Dead Reckoning

Pedestrian Dead Reckoning (PDR) is a core component in pedestrian navigation. Usually, PDR algorithms use the current position and movement information to figure out position in the future in order to accomplish the navigation task. Step detection, as a basic portion of PDR, is significant for the implementation of Pedestrian Navigation. In this paper, a step detection algorithm is designed based on the existing research in the relative area. To improve accuracy, the algorithm involves a Fast Fourier Transformation (FFT) for optimizing. At last, an experiment is conducted for this algorithm, and the error rate of step detection is less than 1%.

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

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