scholarly journals An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System

2021 ◽  
Vol 13 (11) ◽  
pp. 2137
Author(s):  
Bang Wu ◽  
Chengqi Ma ◽  
Stefan Poslad ◽  
David R. Selviah
Keyword(s):  
Human Activity ◽  
Trajectory Optimization ◽  
Inertial Sensors ◽  
Dead Reckoning ◽  
Support Vector ◽  
Positioning System ◽  
Positioning Error ◽  
Door Opening ◽  
Error Accumulation ◽  
Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR), enabled by smartphones’ embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system.

Research on WPS/PDR/MM Integrated Algorithm for Pedestrian Navigation and Positioning

2013 ◽  
Vol 437 ◽  
pp. 870-875 ◽  
Author(s):  
Zhong Liang Deng ◽  
Fei Peng Xie ◽  
Yan Pei Yu ◽  
Xiao Hong Zhao ◽  
Zhuang Yuan
Keyword(s):  
User Experience ◽  
Dead Reckoning ◽  
Positioning System ◽  
Map Matching ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Integrated Method ◽  
Pedestrian Navigation ◽  
Wireless Positioning ◽  
Pedestrian Dead Reckoning

In order to solve the discontinuity of navigation and positioning in indoor signal coverage blind areas, and false region judgment caused by positioning error, an integrated method combining Wireless Positioning System (WPS), Pedestrian Dead Reckoning (PDR) and Map Matching (MM) is presented in this paper. By using the combination of Kalman filtered WPS and PDR information, inertial information and geographic information, pedestrian position could be evaluated. Through experiment, this method effectively increased positioning accuracy of the system as well as greatly improved the user experience.

scholarly journals A New PDR Navigation Device for Challenging Urban Environments

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Miguel Ortiz ◽  
Mathieu De Sousa ◽  
Valerie Renaudin
Keyword(s):  
Inertial Sensors ◽  
Dead Reckoning ◽  
Urban Environments ◽  
Privacy By Design ◽  
Handheld Device ◽  
Location Data ◽  
Pedestrian Navigation ◽  
New Research ◽  
Pedestrian Dead Reckoning ◽  
Original Device

The motivations, the design, and some applications of the new Pedestrian Dead Reckoning (PDR) navigation device, ULISS (Ubiquitous Localization with Inertial Sensors and Satellites), are presented in this paper. It is an original device conceived to follow the European recommendation of privacy by design to protect location data which opens new research toward self-contained pedestrian navigation approaches. Its application is presented with an enhanced PDR algorithm to estimate pedestrian’s footpaths in an autonomous manner irrespective of the handheld device carrying mode: texting or swinging. An analysis of real-time coding issues toward a demonstrator is also conducted. Indoor experiments, conducted with 3 persons, give a 5.8% mean positioning error over the 3 km travelled distances.

A Robust Pedestrian Dead Reckoning System Using Low-Cost Magnetic and Inertial Sensors

2019 ◽  
Vol 68 (8) ◽  
pp. 2996-3003 ◽  
Author(s):  
Ling-Feng Shi ◽  
Yu-Le Zhao ◽  
Gong-Xu Liu ◽  
Sen Chen ◽  
Yue Wang ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Dead Reckoning ◽  
Pedestrian Dead Reckoning

scholarly journals Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ying Guo ◽  
Qinghua Liu ◽  
Xianlei Ji ◽  
Shengli Wang ◽  
Mingyang Feng ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Distance Estimation ◽  
Dead Reckoning ◽  
Principal Component ◽  
Detection Algorithm ◽  
Estimation Accuracy ◽  
Indoor Environments ◽  
Using Data ◽  
The Time Domain ◽  
Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.

scholarly journals Daily Living Movement Recognition for Pedestrian Dead Reckoning Applications

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Alessio Martinelli ◽  
Simone Morosi ◽  
Enrico Del Re
Keyword(s):  
Supervised Learning ◽  
Activity Recognition ◽  
Sport Activity ◽  
Dead Reckoning ◽  
Support Vector ◽  
Navigation Systems ◽  
Cyclic Movements ◽  
Movement Recognition ◽  
Definition Of ◽  
Pedestrian Dead Reckoning

Nowadays, activity recognition is a central topic in numerous applications such as patient and sport activity monitoring, surveillance, and navigation. By focusing on the latter, in particular Pedestrian Dead Reckoning navigation systems, activity recognition is generally exploited to get landmarks on the map of the buildings in order to permit the calibration of the navigation routines. The present work aims to provide a contribution to the definition of a more effective movement recognition for Pedestrian Dead Reckoning applications. The signal acquired by a belt-mounted triaxial accelerometer is considered as the input to the movement segmentation procedure which exploits Continuous Wavelet Transform to detect and segment cyclic movements such as walking. Furthermore, the segmented movements are provided to a supervised learning classifier in order to distinguish between activities such as walking and walking downstairs and upstairs. In particular, four supervised learning classification families are tested: decision tree, Support Vector Machine,k-nearest neighbour, and Ensemble Learner. Finally, the accuracy of the considered classification models is evaluated and the relative confusion matrices are presented.

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