Personal Navigation via High-Resolution Gait-Corrected Inertial Measurement Units

Bebek, Özkan; Suster, Michael A.; Rajgopal, Srihari; Fu, Michael J.; Huang, Xuemei; Çavuşoğlu, M. Cenk; Young, Darrin J.; Mehregany, Mehran; Bogert, Antonie J. van den; Mastrangelo, Carlos H.

doi:10.1109/tim.2010.2046595

Cited by 148 publications

(79 citation statements)

References 15 publications

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“…Eric Foxlin [13] presents such a system as early as in 2005. Recent researches report an error of less than 2% of the total distance [15]. Similar results are also achieved with systems using the SINS method [7].…”

Section: Personal Navigation Systemssupporting

confidence: 74%

An open source inertial sensor network with Bluetooth Smart

Yan

Johns

2015

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

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In this thesis, an open source inertial sensor network with Bluetooth Smart connection is presented. The network has multiple sensor nodes connecting to a consumer electronic device with Bluetooth Smart. Each sensor node contains the following components: (a) an inertial sensor measuring acceleration, angular velocity, and magnetic field with good accuracy; (b) a microcontroller with capacity to handle real-time floating number calculations; (c) a Bluetooth Smart module broadcasting the data with low power consumption. The sensor nodes are designed to be small, allowing the users to wear them conveniently. For demonstration, a basic Personal Navigation System is developed using 4 of these sensor nodes and an Android smartphone. The experiments show that the sensor nodes could output accurate results with small noises when at rest or in slow motion. The example Personal Navigation System could measure total distance walked by a pedestrian with less than 10% error.iii

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Section: Personal Navigation Systemssupporting

confidence: 74%

An open source inertial sensor network with Bluetooth Smart

Yan

Johns

2015

2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

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“…Unfortunately, inertial systems suffer from systematic error accumulation. It is common [12], [13] to address this problem with ZUPT, as proposed in [4]. Although, as pointed out in [14], this solution suffers from many drawbacks and its usefulness is limited.…”

Section: Introductionmentioning

confidence: 99%

Position tracking using inertial and magnetic sensing aided by permanent magnet

Meina

Rykaczewski

Rutkowski

2016

Annals of Computer Science and Information Systems

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Abstract-This paper describes a method for spatial tracking of a strapdown device that can be used for design of humancomputer interfaces. Inertial Measurement Unit (IMU) is used to obtain 6-dof position exploiting the so-called ZUPT technique by the means of the Kalman Filter. Additional corrections of position are done using magnetometer readings in the presence of static magnetic field induced by permanent magnet that overshadow geomagnetic field. This correction allows us to overcome drifting errors of integration of IMU readings. We have also presented comparisons of different models for magnetic field reconstruction that is crucial for this system.

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“…Feliz et al (2009) proposed using a compass and gyroscope to determine trajectories and correct sensor measurements, along with an integrated barometer for height measurement. A similar zero-velocity detector constructed around a pressure sensor mounted beneath the heel of a shoe was used with good results (Bebek et al, 2010). Jiménez et al (2009) implemented and compared several of the most relevant algorithms for step detection, stride length, heading and position estimation.…”

Section: Introductionmentioning

confidence: 99%

A Pedestrian Navigation System Based on Low Cost IMU

Wang

2014

J. Navigation

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For indoor pedestrian navigation with a shoe-mounted inertial measurement unit (IMU), the zero velocity update (ZUPT) technique is implemented to constrain the sensors' error. ZUPT uses the fact that a stance phase appears in each step at zero velocity to correct IMU errors periodically. This paper introduces three main contributions we have achieved based on ZUPT. Since correct stance phase detection is critical for the success of applying ZUPT, we have developed a new approach to detect the stance phase of different gait styles, including walking, running and stair climbing. As the extension of ZUPT, we have proposed a new concept called constant velocity update (CUPT) to correct IMU errors on a moving platform with constant velocity, such as elevators or escalators where ZUPT is infeasible. A closedloop step-wise smoothing algorithm has also been developed to eliminate discontinuities in the trajectory caused by sharp corrections. Experimental results demonstrate the effectiveness of the proposed algorithms. K E Y

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Personal Navigation via High-Resolution Gait-Corrected Inertial Measurement Units

Cited by 148 publications

References 15 publications

An open source inertial sensor network with Bluetooth Smart

An open source inertial sensor network with Bluetooth Smart

Position tracking using inertial and magnetic sensing aided by permanent magnet

A Pedestrian Navigation System Based on Low Cost IMU

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