scholarly journals Step-Detection and Adaptive Step-Length Estimation for Pedestrian Dead-Reckoning at Various Walking Speeds Using a Smartphone

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1423 ◽  
Author(s):  
Ngoc-Huynh Ho ◽  
Phuc Truong ◽  
Gu-Min Jeong
Keyword(s):  
Dead Reckoning ◽  
Step Length ◽  
Step Detection ◽  
Length Estimation ◽  
Walking Speeds ◽  
Adaptive Step ◽  
Pedestrian Dead Reckoning

scholarly journals A Novel Pedestrian Dead Reckoning Algorithm for Multi-Mode Recognition Based on Smartphones

2019 ◽  
Vol 11 (3) ◽  
pp. 294 ◽  
Author(s):  
Limin Xu ◽  
Zhi Xiong ◽  
Jianye Liu ◽  
Zhengchun Wang ◽  
Yiming Ding
Keyword(s):  
Neural Network ◽  
Estimation Method ◽  
Dead Reckoning ◽  
Step Length ◽  
Correction Method ◽  
Step Detection ◽  
Pedestrian Navigation ◽  
Length Estimation ◽  
Multi Mode ◽  
Pedestrian Dead Reckoning

With the rapid development of smartphone technology, pedestrian navigation based on built-in inertial sensors in smartphones shows great application prospects. Currently, most smartphone-based pedestrian dead reckoning (PDR) algorithms normally require a user to hold the phone in a fixed mode and, thus, need to correct the gyroscope heading with inputs from other sensors, which restricts the viability of pedestrian navigation significantly. In this paper, in order to improve the accuracy of the traditional step detection and step length estimation method for different users, a state transition-based step detection method and a step length estimation method using a neural network are proposed. In order to decrease the heading errors and inertial sensor errors in multi-mode system, a multi-mode intelligent recognition method based on a neural network was constructed. On this basis, we propose a heading correction method based on zero angular velocity and an overall correction method based on lateral velocity limitation (LV). Experimental results show that the maximum positioning errors obtained by the proposed algorithm are about 0.9% of the total path length. The proposed novel PDR algorithm dramatically enhances the user experience and, thus, has high value in real applications.

A Robust Step Detection and Stride Length Estimation for Pedestrian Dead Reckoning Using a Smartphone

2020 ◽  
Vol 20 (17) ◽  
pp. 9685-9697
Author(s):  
Yingbiao Yao ◽  
Lei Pan ◽  
Wei Fen ◽  
Xiaorong Xu ◽  
Xuesong Liang ◽  
...  
Keyword(s):  
Stride Length ◽  
Dead Reckoning ◽  
Step Detection ◽  
Length Estimation ◽  
Pedestrian Dead Reckoning

Step length estimation based on D‐ZUPT for pedestrian dead‐reckoning system

2016 ◽  
Vol 52 (11) ◽  
pp. 923-924 ◽  
Author(s):  
Yu Liu ◽  
Shenglong Li ◽  
Chong Mu ◽  
Yingxue Wang
Keyword(s):  
Dead Reckoning ◽  
Step Length ◽  
Length Estimation ◽  
Pedestrian Dead Reckoning

scholarly journals Probabilistic Context-Aware Step Length Estimation for Pedestrian Dead Reckoning

2018 ◽  
Vol 18 (4) ◽  
pp. 1600-1611 ◽  
Author(s):  
Alessio Martinelli ◽  
Han Gao ◽  
Paul D. Groves ◽  
Simone Morosi
Keyword(s):  
Dead Reckoning ◽  
Step Length ◽  
Context Aware ◽  
Length Estimation ◽  
Probabilistic Context ◽  
Pedestrian Dead Reckoning

scholarly journals Axis-Exchanged Compensation and Gait Parameters Analysis for High Accuracy Indoor Pedestrian Dead Reckoning

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Honghui Zhang ◽  
Jinyi Zhang ◽  
Duo Zhou ◽  
Wei Wang ◽  
Jianyu Li ◽  
...  
Keyword(s):  
Dead Reckoning ◽  
Step Length ◽  
Walking Distance ◽  
Analysis Algorithm ◽  
Indoor Location ◽  
Length Estimation ◽  
Gait Parameters ◽  
Parameters Analysis ◽  
Heading Estimation ◽  
Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an effective way for navigation coupled with GNSS (Global Navigation Satellite System) or weak GNSS signal environment like indoor scenario. However, indoor location with an accuracy of 1 to 2 meters determined by PDR based on MEMS-IMU is still very challenging. For one thing, heading estimation is an important problem in PDR because of the singularities. For another thing, walking distance estimation is also a critical problem for pedestrian walking with randomness. Based on the above two problems, this paper proposed axis-exchanged compensation and gait parameters analysis algorithm to improve the navigation accuracy. In detail, an axis-exchanged compensation factored quaternion algorithm is put forward first to overcome the singularities in heading estimation without increasing the amount of computation. Besides, real-time heading is updated by R-adaptive Kalman filter. Moreover, gait parameters analysis algorithm can be divided into two steps: cadence detection and step length estimation. Thus, a method of cadence classification and interval symmetry is proposed to detect the cadence accurately. Furthermore, a step length model adjusted by cadence is established for step length estimation. Compared to the traditional PDR navigation, experimental results showed that the error of navigation reduces 32.6%.

scholarly journals Smartphone Location Recognition: A Deep Learning-Based Approach

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 214 ◽  
Author(s):  
Itzik Klein
Keyword(s):  
Deep Learning ◽  
Sampling Rate ◽  
Dead Reckoning ◽  
Step Length ◽  
Point Of View ◽  
Learning Approaches ◽  
Location Recognition ◽  
Length Estimation ◽  
Recorded Data ◽  
Definition Of

One of the approaches for indoor positioning using smartphones is pedestrian dead reckoning. There, the user step length is estimated using empirical or biomechanical formulas. Such calculation was shown to be very sensitive to the smartphone location on the user. In addition, knowledge of the smartphone location can also help for direct step-length estimation and heading determination. In a wider point of view, smartphone location recognition is part of human activity recognition employed in many fields and applications, such as health monitoring. In this paper, we propose to use deep learning approaches to classify the smartphone location on the user, while walking, and require robustness in terms of the ability to cope with recordings that differ (in sampling rate, user dynamics, sensor type, and more) from those available in the train dataset. The contributions of the paper are: (1) Definition of the smartphone location recognition framework using accelerometers, gyroscopes, and deep learning; (2) examine the proposed approach on 107 people and 31 h of recorded data obtained from eight different datasets; and (3) enhanced algorithms for using only accelerometers for the classification process. The experimental results show that the smartphone location can be classified with high accuracy using only the smartphone’s accelerometers.

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