UPTIME: Ubiquitous pedestrian tracking using mobile phones

Alzantot, Moustafa; Youssef, Moustafa

doi:10.1109/wcnc.2012.6214359

Cited by 175 publications

(114 citation statements)

References 8 publications

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“…1(b) (2). To address this problem, we introduce an integrated optimization algorithm to achieve a globally consistent result.…”

Section: Design Issuesmentioning

confidence: 99%

“…To compare with the stateof-art methods in [2], which achieve a tracking error of 6.9%, Montage achieves a tracking error of about 2.5%.…”

Section: Single User Trackingmentioning

confidence: 99%

“…Some work use the inertial sensors of smartphones with indoor maps to track users as they traverse indoor, e.g., [20]. [2] provides single pedestrian tracking using mobile phones to achieve a tracking error of 6.9%. There are other works dedicated to mobile phone based indoor localization/tracking, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Dead reckoning based approaches, e.g. [2], suffer from accumulated errors. Most of the exiting indoor tracking solutions need a pre-knowledge or at least three anchors.…”

Section: Introductionmentioning

confidence: 99%

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Montage: Combine frames with movement continuity for realtime multi-user tracking

Zhang

Liu

Jiang

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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Abstract-In this work we design and develop Montage for real-time multi-user formation tracking and localization by offthe-shelf smartphones. Montage achieves submeter-level tracking accuracy by integrating temporal and spatial constraints from user movement vector estimation and distance measuring. In Montage we designed a suite of novel techniques to surmount a variety of challenges in real-time tracking, without infrastructure and fingerprints, and without any a priori user-specific (e.g., stride-length and phone-placement) or site-specific (e.g., digitalized map) knowledge. We implemented, deployed and evaluated Montage in both outdoor and indoor environment. Our experimental results (847 traces from 15 users) show that the stridelength estimated by Montage over all users has error within 9cm, and the moving-direction estimated by Montage is within 20 o . For real-time tracking, Montage provides meter-second-level formation tracking accuracy with off-the-shelf mobile phones.

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“…1(b) (2). To address this problem, we introduce an integrated optimization algorithm to achieve a globally consistent result.…”

Section: Design Issuesmentioning

confidence: 99%

“…To compare with the stateof-art methods in [2], which achieve a tracking error of 6.9%, Montage achieves a tracking error of about 2.5%.…”

Section: Single User Trackingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Dead reckoning based approaches, e.g. [2], suffer from accumulated errors. Most of the exiting indoor tracking solutions need a pre-knowledge or at least three anchors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Montage: Combine frames with movement continuity for realtime multi-user tracking

Zhang

Liu

Jiang

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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“…The authors of [16] The authors of [17] also introduced a step-counting method. They collect accelerometer value periodically for a certain period of time, calculate the average and standard deviation of those collected values.…”

Section: Related Workmentioning

confidence: 99%

A Smartphone Indoor Positioning Method

Yim

2013

IJSH

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VR-Fit: Walking-in-Place Locomotion with Real Time Step Detection for VR-Enabled Exercise

Sari

Küçükyılmaz

2019

Mobile Web and Intelligent Information Systems

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With recent advances in mobile and wearable technologies, virtual reality (VR) found many applications in daily use. Today, a mobile device can be converted into a low-cost immersive VR kit thanks to the availability of do-it-yourself viewers in the shape of simple cardboards and compatible software for 3D rendering. These applications involve interacting with stationary scenes or moving in between spaces within a VR environment. VR locomotion can be enabled through a variety of methods, such as head movement tracking, joystick-triggered motion and through mapping natural movements to translate to virtual locomotion. In this study, we implemented a walk-in-place (WIP) locomotion method for a VR-enabled exercise application. We investigate the utility of WIP for exercise purposes, and compare it with joystick-based locomotion in terms of step performance and subjective qualities of the activity, such as enjoyment, encouragement for exercise and ease of use. Our technique uses vertical accelerometer data to estimate steps taken during walking or running, and locomotes the user's avatar accordingly in virtual space. We evaluated our technique in a controlled experimental study with 12 people. Results indicate that the way users control the simulated locomotion affects how they interact with the VR simulation, and influence the subjective sense of immersion and the perceived quality of the interaction. In particular, WIP encourages users to move further, and creates a more enjoyable and interesting experience in comparison to joystick-based navigation.

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UPTIME: Ubiquitous pedestrian tracking using mobile phones

Cited by 175 publications

References 8 publications

Montage: Combine frames with movement continuity for realtime multi-user tracking

Montage: Combine frames with movement continuity for realtime multi-user tracking

A Smartphone Indoor Positioning Method

VR-Fit: Walking-in-Place Locomotion with Real Time Step Detection for VR-Enabled Exercise

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