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Correction: Supuk, T.G., et al. Design, Development and Testing of a Low-Cost sEMG System and Its Use in Recording Muscle Activity in Human Gait. Sensors 2014, 14, 8235–8258
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Sensors 2014, 14(9), 15641-15657; doi:10.3390/s140915641

Hand-Writing Motion Tracking with Vision-Inertial Sensor Fusion: Calibration and Error Correction

1
School of Astronautics, Northwestern Polytechnical University, Xi'an 710000, China
2
Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong, China
3
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China
*
Author to whom correspondence should be addressed.
Received: 7 February 2014 / Revised: 9 August 2014 / Accepted: 14 August 2014 / Published: 25 August 2014
(This article belongs to the Section Physical Sensors)
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Abstract

The purpose of this study was to improve the accuracy of real-time ego-motion tracking through inertial sensor and vision sensor fusion. Due to low sampling rates supported by web-based vision sensor and accumulation of errors in inertial sensors, ego-motion tracking with vision sensors is commonly afflicted by slow updating rates, while motion tracking with inertial sensor suffers from rapid deterioration in accuracy with time. This paper starts with a discussion of developed algorithms for calibrating two relative rotations of the system using only one reference image. Next, stochastic noises associated with the inertial sensor are identified using Allan Variance analysis, and modeled according to their characteristics. Finally, the proposed models are incorporated into an extended Kalman filter for inertial sensor and vision sensor fusion. Compared with results from conventional sensor fusion models, we have shown that ego-motion tracking can be greatly enhanced using the proposed error correction model. View Full-Text
Keywords: MEMS-based motion tracking; vision-based motion tracking; inertial sensor calibration; stochastic error modeling; sensors fusion; human motion tracking MEMS-based motion tracking; vision-based motion tracking; inertial sensor calibration; stochastic error modeling; sensors fusion; human motion tracking
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Zhou, S.; Fei, F.; Zhang, G.; Liu, Y.; Li, W.J. Hand-Writing Motion Tracking with Vision-Inertial Sensor Fusion: Calibration and Error Correction. Sensors 2014, 14, 15641-15657.

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