scholarly journals Pedestrian Walking Distance Estimation Based on Smartphone Mode Recognition

2019 ◽  
Vol 11 (9) ◽  
pp. 1140 ◽  
Author(s):  
Qu Wang ◽  
Langlang Ye ◽  
Haiyong Luo ◽  
Aidong Men ◽  
Fang Zhao ◽  
...  
Keyword(s):  
Stride Length ◽  
Estimation Method ◽  
Distance Estimation ◽  
Dead Reckoning ◽  
Walking Distance ◽  
Estimation Model ◽  
Length Estimation ◽  
Mode Recognition ◽  
Stride Interval ◽  
Normal Speed

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.

scholarly journals Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 840 ◽  
Author(s):  
Qu Wang ◽  
Langlang Ye ◽  
Haiyong Luo ◽  
Aidong Men ◽  
Fang Zhao ◽  
...  
Keyword(s):  
Error Rate ◽  
Stride Length ◽  
Inertial Sensor ◽  
Dead Reckoning ◽  
Training Data ◽  
Sensor Data ◽  
Walking Distance ◽  
Estimation Accuracy ◽  
Length Estimation ◽  
Pedestrian Dead Reckoning

Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian’s stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping).

Evaluation of a New Method of Heading Estimation for Pedestrian Dead Reckoning Using Shoe Mounted Sensors

2005 ◽  
Vol 58 (1) ◽  
pp. 31-45 ◽  
Author(s):  
Ross Stirling ◽  
Ken Fyfe ◽  
Gérard Lachapelle
Keyword(s):  
Urban Areas ◽  
Stride Length ◽  
Performance Metrics ◽  
Dead Reckoning ◽  
Tall Buildings ◽  
Global Navigation Satellite Systems ◽  
Measurement Technology ◽  
Reference Trajectory ◽  
Length Estimation ◽  
Pedestrian Dead Reckoning

In this paper, a novel method of sensor based pedestrian dead reckoning is presented using sensors mounted on a shoe. Sensor based systems are a practical alternative to global navigation satellite systems when positioning accuracy is degraded such as in thick forest, urban areas with tall buildings and indoors. Using miniature, inexpensive sensors it is possible to create self-contained systems using sensor-only navigation techniques optimised for pedestrian motion. The systems developed extend existing foot based stride measurement technology by adding the capability to sense direction, making it possible to determine the path and displacement of the user. The proposed dead-reckoning navigation system applies an array of accelerometers and magneto-resistive sensors worn on the subject's shoe. Measurement of the foot's acceleration allows the precise identification of separate stride segments, thus providing improved stride length estimation. The system relies on identifying the stance phase to resolve the sensor attitude and determine the step heading. Field trials were carried out in forested conditions. Performance metrics include position, stride length estimation and heading with respect to a high accuracy reference trajectory.

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

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.

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 A Step Length Estimation Model of Coefficient Self-Determined Based on Peak-Valley Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Wenxia Lu ◽  
Fei Wu ◽  
Hai Zhu ◽  
Yujin Zhang
Keyword(s):  
Dead Reckoning ◽  
Step Length ◽  
Stride Frequency ◽  
Rule Base ◽  
Portable Devices ◽  
Estimation Model ◽  
Length Estimation ◽  
Average Accuracy ◽  
Acceleration Data ◽  
Essential Components

Without any preinstalled infrastructure, pedestrian dead reckoning (PDR) is a promising indoor positioning technology for pedestrians carrying portable devices to navigate. Step detection and step length estimation (SLE) are two essential components for the pedestrian navigation based on PDR. To solve the overcounting problem, this study proposes a peak-valley detection method, which can remove the abnormal values effectively. The current step length models mostly depend on individual parameters that need to be predetermined for different users. Based on fuzzy logic (FL), we establish a rule base that can adjust the coefficient in the Weinberg model adaptively for every detected step of various human shapes walking. Specifically, to determine the FL rule base, we collect user acceleration data from 10 volunteers walking under the combination of diverse step length and stride frequency, and each one walks 49 times at all. The experimental results demonstrate that our proposed method adapts to different kinds of persons walking at various step velocities. Peak-valley detection can achieve an average accuracy of 99.77% during 500 steps of free walking. Besides, the average errors of 5 testers are all less than 4 m per 100 m and the smallest one is 1.74 m per 100 m using our coefficient self-determined step length estimation model.

scholarly journals Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Haifeng Xing ◽  
Jinglong Li ◽  
Bo Hou ◽  
Yongjian Zhang ◽  
Meifeng Guo
Keyword(s):  
Stride Length ◽  
Nonlinear Models ◽  
Back Propagation ◽  
Dead Reckoning ◽  
Estimation Algorithm ◽  
Position Estimation ◽  
Superior Performance ◽  
Measurement Unit ◽  
Neural Net ◽  
Length Estimation

Pedestrian dead reckoning (PDR) can be used for continuous position estimation when satellite or other radio signals are not available, and the accuracy of the stride length measurement is important. Current stride length estimation algorithms, including linear and nonlinear models, consider a few variable factors, and some rely on high precision and high cost equipment. This paper puts forward a stride length estimation algorithm based on a back propagation artificial neural network (BP-ANN), using a consumer-grade inertial measurement unit (IMU); it then discusses various factors in the algorithm. The experimental results indicate that the error of the proposed algorithm in estimating the stride length is approximately 2%, which is smaller than that of the frequency and nonlinear models. Compared with the latter two models, the proposed algorithm does not need to determine individual parameters in advance if the trained neural net is effective. It can, thus, be concluded that this algorithm shows superior performance in estimating pedestrian stride length.

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