Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm

Xing, Haifeng; Li, Jinglong; Hou, Bo; Zhang, Yongjian; Guo, Mei

doi:10.1155/2017/6091261

Cited by 62 publications

(49 citation statements)

References 23 publications

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“…In addition, to verify the performance compared with a deep learning-based model, we performed the experiment using the ANN-based model [35] that uses five parameters as the input closely related to the stride length and one output to estimate the corresponding stride length. For this experiment, we reconfigured the existing dataset with a pair of five parameters (e.g., stride frequency, maximum, standard deviation, mean of acceleration, and height of subject) and stride length, and these parameters and stride length were extracted from the measured IMU sensory signal and corrected GPS trajectory, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Edel et al [34] proposed a step recognition method based on BLSTM (bidirectional long-short term memory)-RNNs with three-axis acceleration from a smartphone. In Xing et al [35], five manually-designed parameters from the IMU signal that are attached to the top of the foot and closely related to the linear walking model are fed into the artificial neural network (ANN) as input, and the desired network output is the stride length obtained through a regression. Hannick et al [36,37] proposed a CNN-based stride length estimation and gait parameter extraction methods using publicly available datasets collected through an IMU attached to the shoe.…”

Section: Related Workmentioning

confidence: 99%

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

Kang

Lee

Eom

2018

Sensors

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We introduce a novel method for indoor localization with the user’s own smartphone by learning personalized walking patterns outdoors. Most smartphone and pedestrian dead reckoning (PDR)-based indoor localization studies have used an operation between step count and stride length to estimate the distance traveled via generalized formulas based on the manually designed features of the measured sensory signal. In contrast, we have applied a different approach to learn the velocity of the pedestrian by using a segmented signal frame with our proposed hybrid multiscale convolutional and recurrent neural network model, and we estimate the distance traveled by computing the velocity and the moved time. We measured the inertial sensor and global position service (GPS) position at a synchronized time while walking outdoors with a reliable GPS fix, and we assigned the velocity as a label obtained from the displacement between the current position and a prior position to the corresponding signal frame. Our proposed real-time and automatic dataset construction method dramatically reduces the cost and significantly increases the efficiency of constructing a dataset. Moreover, our proposed deep learning model can be naturally applied to all kinds of time-series sensory signal processing. The performance was evaluated on an Android application (app) that exported the trained model and parameters. Our proposed method achieved a distance error of <2.4% and >1.5% on indoor experiments.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Kang

Lee

Eom

2018

Sensors

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“…Additionally, it might be possible to robust in different implementation of inertial-based mobile IPS systems [38] [39]. The results of the study could also significantly contribute to modernising current location detection systems, and provide useful finding for other inertial-based positioning systems studies [40] [41] [42] [43]. Next following section will discuss about the proposed method of special strategies resampling algorithm.…”

Section: Objectivementioning

confidence: 99%

Design and Implementation of the RSIL to LLVM IR Translator for Verification of the Intermediate Code on IoT Virtual Machine

Son¹,

Oh²,

Lee³

2017

IJCA

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The phenomenon of sample impoverishment during particle filtering always contribute computation burden to the inertial-based mobile IPS systems. This is due to the factor of noise measurement and number of particle. Usually, the special strategies resampling algorithms was used. However, these algorithms just can fit in certain environment. This needs an adaptation of noise measurement and number of particle in a algorithm in order to make resampling with more intelligence, reliability and robust. In tbis paper, we will propose an adaptive special strategies resampling by adapting noise and particle measurement. These adaptation is used to determine the most suitable algorithm of special strategies resampling, that can be switched for resampling purpose. Finally, we illustrate our proposed solution our for indoor environment setup.

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“…Only four features are considered in this work, i.e., the maximal value, the minimal value, the variance and the integral of the acceleration magnitude from a step. In [10] the authors also use a neural network to predict the step length but this time with a foot-mounted accelerometer. In this work, five features are used, i.e., the mean stride frequency, the maximal acceleration, the standard deviation of the acceleration, the mean acceleration and the height of the test person.…”

Section: Introductionmentioning

confidence: 99%

“…In [9,10], the authors use, respectively, only four and five self-chosen features that can be useful to estimate the step length. However, the authors do not verify if each of these features is indeed useful and if other features can be used to achieve better performance.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection for Machine Learning Based Step Length Estimation Algorithms

Vandermeeren

Bruneel

Steendam

2020

Sensors

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An accurate step length estimation can provide valuable information to different applications such as indoor positioning systems or it can be helpful when analyzing the gait of a user, which can then be used to detect various gait impairments that lead to a reduced step length (caused by e.g., Parkinson’s disease or multiple sclerosis). In this paper, we focus on the estimation of the step length using machine learning techniques that could be used in an indoor positioning system. Previous step length algorithms tried to model the length of a step based on measurements from the accelerometer and some tuneable (user-specific) parameters. Machine-learning-based step length estimation algorithms eliminate these parameters to be tuned. Instead, to adapt these algorithms to different users, it suffices to provide examples of the length of multiple steps for different persons to the machine learning algorithm, so that in the training phase the algorithm can learn to predict the step length for different users. Until now, these machine learning algorithms were trained with features that were chosen intuitively. In this paper, we consider a systematic feature selection algorithm to be able to determine the features from a large collection of features, resulting in the best performance. This resulted in a step length estimator with a mean absolute error of 3.48 cm for a known test person and 4.19 cm for an unknown test person, while current state-of-the-art machine-learning-based step length estimators resulted in a mean absolute error of 4.94 cm and 6.27 cm for respectively a known and unknown test person.

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Pedestrian Stride Length Estimation from IMU Measurements and ANN Based Algorithm

Cited by 62 publications

References 23 publications

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

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

Design and Implementation of the RSIL to LLVM IR Translator for Verification of the Intermediate Code on IoT Virtual Machine

Feature Selection for Machine Learning Based Step Length Estimation Algorithms

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