scholarly journals Walking Distance Estimation Using Walking Canes with Inertial Sensors

Sensors ◽  
2018 ◽  
Vol 18 (1) ◽  
pp. 230 ◽  
Author(s):  
Duc Dang ◽  
Young Suh
Keyword(s):  
Inertial Sensors ◽  
Distance Estimation ◽  
Walking Distance

scholarly journals IMPROVED INDOOR POSITIONING USING BLE DIFFERENTIAL DISTANCE CORRECTION AND PEDESTRIAN DEAD RECKONING

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 193-198
Author(s):  
Y. T. Tang ◽  
Y. T. Kuo ◽  
J. K. Liao ◽  
K. W. Chiang
Keyword(s):  
Inertial Sensors ◽  
Distance Estimation ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Walking Distance ◽  
Indoor Environments ◽  
Alternative Scheme ◽  
Differential Distance ◽  
Pedestrian Dead Reckoning ◽  
Distance Correction

Abstract. Recently, indoor positioning becomes a popular issue because of its corresponding location-aware applications. Owing to the limits of the sheltered signal of satellites in indoor environments, one of the alternative scheme is Bluetooth Low Energy (BLE) technology. BLE device broadcasts Received Signal Strength Indicator (RSSI) for distance estimation and further positioning. However, in the complex indoor environment, the reflection, fading, and multipath effect of BLE make the variable RSSI and may lead to poor quality of RSSI. In this study, the concept called Differential Distance Correction (DDC) is similar to the Differential Global Navigation Satellite System (DGNSS). This method can eliminate some residuals and further improve the results with the corrected distance. On the other hand, Pedestrian Dead Reckoning (PDR) is another common indoor positioning method. PDR can propagate the next position from the current position by the implemented of inertial sensors. Despite that, the error of inertial sensors would accumulate with time and walking distance, which position update is required for restraining the drift. Accordingly, the two indoor positioning methods have their strong and weak point. BLE-based positioning is absolute positioning, while PDR is relative positioning. This study proposes a concept that combines the two methods. The pedestrian receives the RSSI and records the information from inertial sensors simultaneously. Through the complementary of two methods, the positioning results would be improved from 29% to 66% according to different travelled distance.

scholarly journals Walking distance estimation using multi-agent simulation of pedestrian flows

2018 ◽  
Vol 136 ◽  
pp. 489-498 ◽  
Author(s):  
Ivan Khodnenko ◽  
Sergei Kudinov ◽  
Egor Smirnov
Keyword(s):  
Distance Estimation ◽  
Walking Distance ◽  
Agent Simulation ◽  
Multi Agent

scholarly journals An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices

2020 ◽  
Author(s):  
Bo Zhao ◽  
Chao Zheng ◽  
Xinxin Ren ◽  
Jianrong Dai
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Optimization Method ◽  
Contact Tracing ◽  
Walking Distance ◽  
Estimation Methods ◽  
Smart Devices ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model

Distance estimation methods arise in many applications, such as indoor positioning and Covid-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide the high accuracy of walking distance and direction, which is used to compensate for the effects of interference on the RSSI. Moreover, the parameters of the path loss model are optimized to dynamically fit to the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor <a>environments</a> and is also compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with the improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, in comparison with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.

scholarly journals An Optimization Method Combining RSSI and PDR Data to Estimate Distance Between Smart Devices

2020 ◽  
Author(s):  
Bo Zhao ◽  
Chao Zheng ◽  
Xinxin Ren ◽  
Jianrong Dai
Keyword(s):  
Path Loss ◽  
Distance Estimation ◽  
Optimization Method ◽  
Contact Tracing ◽  
Walking Distance ◽  
Estimation Methods ◽  
Smart Devices ◽  
Indoor Environments ◽  
Loss Model ◽  
Path Loss Model

Distance estimation methods arise in many applications, such as indoor positioning and Covid-19 contact tracing. The received signal strength indicator (RSSI) is favored in distance estimation. However, the accuracy is not satisfactory due to the signal fluctuation. Besides, the RSSI-only method has a large ranging error because it uses fixed parameters of the path loss model. Here, we propose an optimization method combining RSSI and pedestrian dead reckoning (PDR) data to estimate the distance between smart devices. The PDR may provide the high accuracy of walking distance and direction, which is used to compensate for the effects of interference on the RSSI. Moreover, the parameters of the path loss model are optimized to dynamically fit to the complex electromagnetic environment. The proposed method is evaluated in outdoor and indoor <a>environments</a> and is also compared with the RSSI-only method. The results show that the mean absolute error is reduced up to 0.51 m and 1.02 m, with the improvement of 10.60% and 64.55% for outdoor and indoor environments, respectively, in comparison with the RSSI-only method. Consequently, the proposed optimization method has better accuracy of distance estimation than the RSSI-only method, and its feasibility is demonstrated through real-world evaluations.

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.

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