Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

Kang, Jiheon; Lee, Joonbeom; Eom, Doo Seop

doi:10.3390/s18093149

Cited by 41 publications

(46 citation statements)

References 41 publications

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“…In this case, the estimated distances between P and AP actually equals 1 . However, it can still be calculated by (6).…”

Section: Methodsmentioning

confidence: 99%

“…It is well known that a location can be estimated from various measurement models such as distance-based and orientation-based measurement models [6,7]. Generally, there are four types of measurements used for Wi-Fi and smartphone-based indoor positioning: time of arrival (ToA) [8,9], time difference of arrival (TDoA) [10,11], angle of arrival (AoA) [12], and received signal strength indicator (RSSI) based models [13][14][15].…”

Section: Measurement Modelmentioning

confidence: 99%

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A New Approach for Wi-Fi-Based People Localization in a Long Narrow Space

Gao

Tang

Bai

2019

Wireless Communications and Mobile Computing

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Wi-Fi-based positioning technology has been recognized as an effective technology for indoor positioning along with the rapid development and application of smartphones. One of its typical applications is localizing people in large public areas such as shopping malls, schools, and airports. A common and critical task from such applications is localizing people in long narrow spaces such as a long corridor which is considered as the most frequent place where people activities take place. Generally, the geographical distribution of Wi-Fi access points (APs) in long spaces is poor for localizing people since normally less than 3 APs are connected to a smartphone. In addition, all these APs are normally mounted along a straight line; hence, it is difficult to track people using traditional positioning algorithms such as trilateration and fingerprinting. To address this issue, a new approach called same-line-dual-connection (SLDC) was developed to estimate user locations with a good positioning accuracy, particularly for long narrow spaces where only limited Wi-Fi connections are available. The SLDC approach integrates geometry principle with positioning theories and machine learning ideas. The test outcome has shown that the SLDC approach produced a promising result, and a mean positioning accuracy of 1.60 m was achieved.

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“…In this case, the estimated distances between P and AP actually equals 1 . However, it can still be calculated by (6).…”

Section: Methodsmentioning

confidence: 99%

Section: Measurement Modelmentioning

confidence: 99%

A New Approach for Wi-Fi-Based People Localization in a Long Narrow Space

Gao

Tang

Bai

2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…For instance, in reference [28], the author proposed an accurate estimation method of walking speed using deep learning for smartphone-based PDR. Some researchers applied a method to learn the speed of pedestrians by using segmented signal frames and mixed multi-scale convolution and recurrent neural network models, and estimated the travel distance by calculating the speed and moving time [29]. In the paper [30], the author used the deep learning method to process the pictures taken by the smartphone camera, then identifies the user's location, and uses the particle filter algorithm constrained by the scene information to determine the final location.…”

Section: Introductionmentioning

confidence: 99%

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

Huang

et al. 2020

Sensors

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In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning.

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“…where f is the step frequency, which represents the reciprocal of one stride interval, v is the acceleration variance during the interval of one step, α and β denote the weighting factors of step frequency and acceleration variance, respectively, and γ represents a constant that is used to fit the relationship between the actual distance and the estimated distance. Kang et al [31] simultaneously measured the inertial sensor and global positioning system (GPS) position while walking outdoors with a reliable GPS fix, and regarded the velocity from the GPS as labels to train a hybrid multiscale convolutional and recurrent neural network model. After that, Kang leveraged the prediction velocity and moving time to estimate the traveled distance.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

Wang

Luo

et al. 2019

Remote Sensing

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Stride length and walking distance estimation are becoming a key aspect of many applications. One of the methods of enhancing the accuracy of pedestrian dead reckoning is to accurately estimate the stride length of pedestrians. Existing stride length estimation (SLE) algorithms present good performance in the cases of walking at normal speed and the fixed smartphone mode (handheld). The mode represents a specific state of the carried smartphone. The error of existing SLE algorithms increases in complex scenes with many mode changes. Considering that stride length estimation is very sensitive to smartphone modes, this paper focused on combining smartphone mode recognition and stride length estimation to provide an accurate walking distance estimation. We combined multiple classification models to recognize five smartphone modes (calling, handheld, pocket, armband, swing). In addition to using a combination of time-domain and frequency-domain features of smartphone built-in accelerometers and gyroscopes during the stride interval, we constructed higher-order features based on the acknowledged studies (Kim, Scarlett, and Weinberg) to model stride length using the regression model of machine learning. In the offline phase, we trained the corresponding stride length estimation model for each mode. In the online prediction stage, we called the corresponding stride length estimation model according to the smartphone mode of a pedestrian. To train and evaluate the performance of our SLE, a dataset with smartphone mode, actual stride length, and total walking distance were collected. We conducted extensive and elaborate experiments to verify the performance of the proposed algorithm and compare it with the state-of-the-art SLE algorithms. Experimental results demonstrated that the proposed walking distance estimation method achieved significant accuracy improvement over existing individual approaches when a pedestrian was walking in both indoor and outdoor complex environments with multiple mode changes.

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Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization

Cited by 41 publications

References 41 publications

A New Approach for Wi-Fi-Based People Localization in a Long Narrow Space

A New Approach for Wi-Fi-Based People Localization in a Long Narrow Space

Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning

Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

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