Comparison and evaluation of acceleration based step length estimators for handheld devices

Jahn, Jasper; Batzer, Ulrich; Seitz, Jochen; Patino-Studencka, Lucila; Boronat, Javier Gutierrez

doi:10.1109/ipin.2010.5646888

Cited by 97 publications

(57 citation statements)

References 5 publications

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“…[20,21]. Here the acceleration on the PCOM (Pedestrian's Centre Of Mass) is measured, which is the point at the back near the fourth lumbar vertebra [22]. The estimated step length is calculated as…”

Section: Pdrmentioning

confidence: 99%

Building up an Inertial Navigation System Using Standard Mobile Devices

Gunia¹,

Wu²,

Joram³

et al. 2017

JEE

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This paper presents the development of a PNS (Pedestrian Navigation System), which utilizes accelerometer, gyroscope and magnetometer data to enable accurate positioning. Therefore, the sensor basics as well as the mathematics regarding reference frames and coordinate transformations are introduced initially. Particular focus is given to quaternions, since they provide a performance-effective means to execute rotations. In great detail the two distinct approaches for a PNS are introduced, i.e. INS (inertial navigation systems) and PDR (pedestrian dead reckoning). For each, a comparison of state-of-the-art techniques is presented. Special attention is paid to orientation estimation and stance phase detection. Our system combines the most promising techniques and describes improvements, whose usefulness becomes obvious in our experiments. We have applied our PNS in five different test scenarios. For the most complex rectangular-shaped use case, we achieve on average error of 1.66% with regard to the total travelled distance, which is superior to other recent PNS utilizing comparable sensors.

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Section: Pdrmentioning

confidence: 99%

Building up an Inertial Navigation System Using Standard Mobile Devices

Gunia¹,

Wu²,

Joram³

et al. 2017

JEE

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“…There are several step length estimation methods that have been proposed for different applications in the past [21][22][23]. A comparison of several popular methods has revealed that the one proposed by Weinberg et al is best suited for a waist-mounted IMU [16,26].…”

Section: Knowledge-based Sle Algorithm Based On Fuzzy Logicmentioning

confidence: 99%

A Knowledge-Based Step Length Estimation Method Based on Fuzzy Logic and Multi-Sensor Fusion Algorithms for a Pedestrian Dead Reckoning System

Lai

Chang

Tsai

et al. 2016

IJGI

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Abstract:The demand for pedestrian navigation has increased along with the rapid progress in mobile and wearable devices. This study develops an accurate and usable Step Length Estimation (SLE) method for a Pedestrian Dead Reckoning (PDR) system with features including a wide range of step lengths, a self-contained system, and real-time computing, based on the multi-sensor fusion and Fuzzy Logic (FL) algorithms. The wide-range SLE developed in this study was achieved by using a knowledge-based method to model the walking patterns of the user. The input variables of the FL are step strength and frequency, and the output is the estimated step length. Moreover, a waist-mounted sensor module has been developed using low-cost inertial sensors. Since low-cost sensors suffer from various errors, a calibration procedure has been utilized to improve accuracy. The proposed PDR scheme in this study demonstrates its ability to be implemented on waist-mounted devices in real time and is suitable for the indoor and outdoor environments considered in this study without the need for map information or any pre-installed infrastructure. The experiment results show that the maximum distance error was within 1.2% of 116.51 m in an indoor environment and was 1.78% of 385.2 m in an outdoor environment.

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“…The rationale is that of using a dead reckoning-like approach [8,9]: detect the human steps by means of a proper analysis of the accelerometer measurements [33], then use the combination of magnetometer and accelerometer measurements to estimate the movement direction with respect to the north [28].…”

Section: System Description and Relation To Previous Workmentioning

confidence: 99%

“…An estimation of the initial position (u 0 , v 0 ) is assumed to be (a priori) available. The step length s t is assumed to be provided by a proper analysis of the accelerator measurements [33]. Alternatively, s t can be fixed to a constant value (an approximation of the mean step length): the tracking algorithm described in Section 3 is designed to compensate (relatively small) step length errors.…”

Section: System Description and Relation To Previous Workmentioning

confidence: 99%

A Particle Filter for Smartphone-Based Indoor Pedestrian Navigation

et al. 2014

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This paper considers the problem of indoor navigation by means of low-cost mobile devices. The required accuracy, the low reliability of low-cost sensor measurements and the typical unavailability of the GPS signal make indoor navigation a challenging problem. In this paper, a particle filtering approach is presented in order to obtain good navigation performance in an indoor environment: the proposed method is based on the integration of information provided by the inertial navigation system measurements, the radio signal strength of a standard wireless network and of the geometrical information of the building. In order to make the system as simple as possible from the user's point of view, sensors are assumed to be uncalibrated at the beginning of the navigation, and an auto-calibration procedure of the magnetic sensor is performed to improve the system performance: the proposed calibration procedure is performed during regular user's motion (no specific work is required). The navigation accuracy achievable with the proposed method and the results of the auto-calibration procedure are evaluated by means of a set of tests carried out in a university building.

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Comparison and evaluation of acceleration based step length estimators for handheld devices

Cited by 97 publications

References 5 publications

Building up an Inertial Navigation System Using Standard Mobile Devices

Building up an Inertial Navigation System Using Standard Mobile Devices

A Knowledge-Based Step Length Estimation Method Based on Fuzzy Logic and Multi-Sensor Fusion Algorithms for a Pedestrian Dead Reckoning System

A Particle Filter for Smartphone-Based Indoor Pedestrian Navigation

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