Quaternion-Based Kalman Filter With Vector Selection for Accurate Orientation Tracking

Zhang, Zhiqiang; Meng, Xiaohong; Wu, Jiankang

doi:10.1109/tim.2012.2196397

Cited by 71 publications

(47 citation statements)

References 17 publications

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“…Biomotion+ and NAO Robot For motion capture, the Biomotion+ platform developed by the Hamlyn Centre has been used. It is an inertial sensor based motion tracking system involving sensor calibration, sensor to body segment alignment and body segment orientation estimation [31,32,33]. Capturing full body movement requires the placement of nine sensor nodes on the sternum, upper arms, forearms, thighs, and shanks of the subject.…”

Section: Dynamic Walking Imitation Methodsmentioning

confidence: 99%

Imitation of Dynamic Walking with BSN for a Humanoid Robot

Teachasrisaksakul

Zhang

Yang

et al. 2015

IEEE J. Biomed. Health Inform.

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Section: Dynamic Walking Imitation Methodsmentioning

confidence: 99%

Imitation of Dynamic Walking with BSN for a Humanoid Robot

Teachasrisaksakul

Zhang

Yang

et al. 2015

IEEE J. Biomed. Health Inform.

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“…There are also plenty of methods to fuse the inertial/magnetic sensor measurements to estimate the sensor orientation difference between time t and k [26] [27]. As shown in the Fig.…”

Section: A Simplified Hand-eye Equation Derivationmentioning

confidence: 99%

Cameras and Inertial/Magnetic Sensor Units Alignment Calibration

Zhang

2016

IEEE Trans. Instrum. Meas.

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Abstract-Due to the external acceleration interference/magnetic disturbance, the inertial/magnetic measurements are usually fused with visual data for drift-free orientation estimation, which plays an important role for a wide variety of applications, ranging from virtual reality, robot, computer vision, to bio-motion analysis and navigation. However, in order to perform data fusion, alignment calibration must be performed in advance to determine the difference between the sensor coordinate system and camera coordinate system. Since orientation estimation performance of the inertial/magnetic sensor unit is immune to the selection of the inertial/magnetic sensor frame original point, we therefore ignore the translational difference by assuming the sensor and camera coordinate systems sharing the same original point and focus on the rotational alignment difference only in this paper. By exploiting the intrinsic restrictions among the coordinate transformations, the rotational alignment calibration problem is formulated by a simplified hand-eye equation AX = XB (A, X and B are all rotation matrices). A two-step iterative algorithm is then proposed to solve such simplified hand-eye calibration task. Detailed laboratory validation has been performed and the good experimental results have illustrated the effectiveness of the proposed alignment calibration method.

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“…The basic idea for such schemes is to detect whether the sensor measurements are perturbed and then replace the degraded measurements with more reliable ones. Lee et al [105] and Zhang et al [106] have explored such ideas in their work. In general, noise adjustment has shown better performance in reducing the effects of interference from acceleration and magnetic disturbances than interference estimation methods.…”

Section: ) Noise Adjustmentmentioning

confidence: 99%

Wearable Sensing for Solid Biomechanics

Wong

Zhang

et al. 2015

IEEE Sensors J.

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Abstract-Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in healthcare, sport, wellbeing, and workflow analysis. Conventional laboratorybased biomechanical analysis systems and observation-based tests are only designed to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing upon sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations that can affect the accuracy and robustness of existing methods, and different methods for reducing the impact of these sources of errors are described in this review. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, as well as the future direction of sensing for biomechanics are also discussed.

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Quaternion-Based Kalman Filter With Vector Selection for Accurate Orientation Tracking

Cited by 71 publications

References 17 publications

Imitation of Dynamic Walking with BSN for a Humanoid Robot

Imitation of Dynamic Walking with BSN for a Humanoid Robot

Cameras and Inertial/Magnetic Sensor Units Alignment Calibration

Wearable Sensing for Solid Biomechanics

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