A Novel Pedestrian Dead Reckoning Algorithm for Multi-Mode Recognition Based on Smartphones

Xu, Limin; Xiong, Zhi; Liu, Jianye; Wang, Zhengchun; Ding, Yiming

doi:10.3390/rs11030294

Cited by 37 publications

(17 citation statements)

References 23 publications

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“…In [1], the neural network is implemented in a multi-mode intelligent recognition approach based on the smartphone's sensors. The empirical results presented a maximum positioning error lower than 1% of the total path distance.…”

Section: Advances In Science Technology and Engineering Systems Journalmentioning

confidence: 99%

“…Lately, the step count has been extensively used in various applications, such as health monitoring, auxiliary positioning, and gaming. Earlier, the Pedestrian Dead Reckoning (PDR) was used as a localization method based on advanced built-in sensors [1] that exist these days in Smartphones. These sensors found in the smartphones include accelerometers, proximity sensors, gyroscopes, magnetometers, and Global Positioning System.…”

Section: Introductionmentioning

confidence: 99%

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Algorithm Design for Accurate Steps Counting Based on Smartphone Sensors for Indoor Applications

Muhsen¹,

Al-Amaydeh²,

Al-Hamlan³

2020

Adv. sci. technol. eng. syst. j.

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This paper aims to design an accurate and robust algorithm for counting the steps based on the smartphone's accelerometer for indoor applications. Different daily activities have been considered in the experimental scenarios, including normal walking with various smartphone positions and running activities. The detection process's accuracy is ensured by setting an appropriate segregation approach and defining a fixed threshold for each experiment. The algorithm generates a new envelope signal that mimics the collected steps signal without the vibrations and noise elements to avoid miscounting the exact steps. The followed approach results have been compared with the pedometer applications on the android platform and HUAWEI Watch GT-731. The proposed algorithm showed improved detection accuracy in contrast to the other literature approaches. The obtained accuracy was around 99% for the standard walking scenario and approximately 97% for the running activity scenario.

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Section: Advances In Science Technology and Engineering Systems Journalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algorithm Design for Accurate Steps Counting Based on Smartphone Sensors for Indoor Applications

Muhsen¹,

Al-Amaydeh²,

Al-Hamlan³

2020

Adv. sci. technol. eng. syst. j.

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“…For instance, a wrist-mounted inertial localization system needs to cope with the fact that the motion of the arm is detached from the motion of the body. Therefore, it is challenging to track the pedestrian's position when the hand is not swinging [6,7]. Another challenge of inertial localization systems is related to the integration of the errors in the IMU measurements.…”

Section: Introductionmentioning

confidence: 99%

Automatic Calibration of the Step Length Model of a Pocket INS by Means of a Foot Inertial Sensor

Ahmed

Diaz

Domínguez

2020

Sensors

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All non-foot-mounted inertial localization systems have a common challenge: the need for calibrating the parameters of the step length model. The calibration of the parameters of a step length model is key for an accurate estimation of the pedestrian's step length, and therefore, for the accuracy of the position estimation. In a previous work, we provided a proof of concept on how to calibrate step length models with a foot inertial navigation system (INS), i.e., an INS based on an inertial measurement unit (IMU) mounted on the upper front part of the foot. The reason is that the foot INS does not require calibration thanks to the implementation of the strapdown algorithm. The goal of this article is to automatically calibrate the parameters of a step length model of the pocket INS by means of the foot INS. The step length model of the pocket INS has two parameters: the slope and offset of a first-order linear regression that relates the amplitude of the thigh pitch with the user's step length. Firstly, we show that it is necessary to estimate the two parameters of the step length model. Secondly, we propose a method to automatically estimate these parameters by means of a foot INS. Finally, we propose a practical implementation of the proposed method in the pocket INS. We evaluate the pocket INS with the proposed calibration method and we compare the results to the state of the art implementations of the pocket INS. The results show that the proposed automatic calibration method outperforms the previous work, which proves the need for calibrating all the parameters of the step length model of the pocket INS. In this work, we conclude that it is possible to use a foot INS to automatically calibrate all parameters of the step length model of the pocket INS. Since the calibration of the step length model is always needed, our proposed automatic calibration method is a key enabler for using the pocket INS.

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“…Indoor positioning technology, such as Wi-Fi [1,2], magnetic [3,4], pedestrian dead reckoning [5,6] and visible light [7,8] technologies, have become increasingly more important in people's daily life, and positioning services have gradually become an indispensable mobile application. Most Wi-Fi based positioning technologies do not require deploying additional hardware because they only utilize Wi-Fi hotspots and existing wireless LANs (Wireless Local Area Networks) to obtain position estimation.…”

Section: Introductionmentioning

confidence: 99%

A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron

Wang

Luo

et al. 2019

Remote Sensing

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With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with time, extracting robust features of Wi-Fi signals is the key point to maintaining good positioning accuracy. To handle the dynamic fluctuation with time and sparsity of Wi-Fi signals, we propose an SDAE (Stacked Denoising Autoencoder)-based feature extraction method, which can obtain a robust and time-independent Wi-Fi fingerprint by learning the reconstruction distribution from a raw Wi-Fi signal and an artificial-noise-added Wi-Fi signal. We also leverage the strong representation ability of MLP (Multi-Layer Perceptron) to build a regression model, which maps the extracted features to the corresponding location. To fully evaluate the performance of our proposed algorithm, three datasets are applied, which represent three different scenarios, namely, spacious area with time interval, no time interval, and complex area with large time interval. The experimental results confirm the validity of our proposed SDAE-based feature extraction method, which can accurately reflect Wi-Fi signals in corresponding locations. Compared with other regression models, our proposed regression model can better map the extracted features to the target position. The average positioning error of our proposed algorithm is 4.24 m when there is a 52-day interval between training dataset and testing dataset. That confirms that the proposed algorithm outperforms other state-of-the-art positioning algorithms when there is a large time interval between training dataset and testing dataset. present, due to the wide deployment and availability of Wi-Fi infrastructure, Wi-Fi fingerprint-based localization has become one of the most dominant indoor positioning techniques.There are two main types of Wi-Fi-based indoor positioning technologies: RSSI (Received Signal Strength Indicator)-based ranging positioning algorithm [9][10][11], and fingerprint-based positioning algorithm [12][13][14]. The RSSI-based ranging positioning algorithm [11] usually adopts the received Wi-Fi signal to estimate the distance between the target (its location is unknown) and the access point (its location is known) using the wireless radio signal propagation model, and then estimates the target position using trilateration or multilateration methods. The fingerprint-based positioning algorithm [14] adopts the signal matching algorithm to estimate the user location. It first collects environmental Wi-Fi signals and constructs a Wi-Fi fingerprint database during the offline phase. During the online positioning phase, the fingerprint-based positioning algorithm compares the current Wi-Fi observation with the recorded fingerprint in the database to obtain the target position using the optimum matching criterion. Compared with the fingerprint-based positioning algorithm, the RSSI-based ranging positioning algorithm struggles to meet...

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A Novel Pedestrian Dead Reckoning Algorithm for Multi-Mode Recognition Based on Smartphones

Cited by 37 publications

References 23 publications

Algorithm Design for Accurate Steps Counting Based on Smartphone Sensors for Indoor Applications

Algorithm Design for Accurate Steps Counting Based on Smartphone Sensors for Indoor Applications

Automatic Calibration of the Step Length Model of a Pocket INS by Means of a Foot Inertial Sensor

A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron

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