The measurement of human motion: A comparison of commercially available systems

Richards, James G.

doi:10.1016/s0167-9457(99)00023-8

Cited by 310 publications

(232 citation statements)

References 5 publications

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“…The cameras tracked the position of the retro-reflective markers in threedimension and this system has demonstrated a reconstruction error of <1mm (Ehara, Fujimoto, Miyazaki, Tanaka, & Yamamoto, 1995;Richards, 1999). Participants were therefore fitted with a set of retro-reflective body markers on their preferred hand, forearm and upper arm in accordance with the calibrated anatomical systems technique, as previously described (Campbell & Alderson, 2009;Campbell & Lloyd, 2009;Mitchinson, Campbell, Oldmeadow, Gibson, & Hopper, 2012;Wu et al, 2005).…”

Section: Variablesmentioning

confidence: 99%

A comparison of the upper limb movement kinematics utilized by children playing virtual and real table tennis

Bufton

Campbell

Howie

et al. 2014

Human Movement Science

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Active virtual games (AVG) may facilitate gross motor skill development, depending on their fidelity. This study compared the movement patterns of nineteen 10-12yr old children, whilst playing contributed to the differences in movement patterns observed. Understanding the interface components for AVGs may help development of higher fidelity games to potentially enhance the development of gross motor skill and thus participation in PA.

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Section: Variablesmentioning

confidence: 99%

A comparison of the upper limb movement kinematics utilized by children playing virtual and real table tennis

Bufton

Campbell

Howie

et al. 2014

Human Movement Science

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“…The pose error of indoor quadcopters have been determined before with indoor motion tracking and measurement systems, such as the Vicon motion tracking system which has been shown to have millimetre-levels of accuracy [4]. However, these results and measurement methods are not applicable in the outdoor case, since testing indoors would deny a quadcopter its GPS coordinates.…”

Section: Introductionmentioning

confidence: 99%

An investigation into multi-dimensional prediction models to estimate the pose error of a quadcopter in a CSP plant setting

Lock

Smit

Treurnicht

2016

AIP Conference Proceedings

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Abstract. The Solar Thermal Energy Research Group (STERG) is investigating ways to make heliostats cheaper to reduce the total cost of a concentrating solar power (CSP) plant. One avenue of research is to use unmanned aerial vehicles (UAVs) to automate and assist with the heliostat calibration process. To do this, the pose estimation error of each UAV must be determined and integrated into a calibration procedure. A computer vision (CV) system is used to measure the pose of a quadcopter UAV. However, this CV system contains considerable measurement errors. Since this is a high-dimensional problem, a sophisticated prediction model must be used to estimate the measurement error of the CV system for any given pose measurement vector. This paper attempts to train and validate such a model with the aim of using it to determine the pose error of a quadcopter in a CSP plant setting.

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“…MLDs images, as feature-based motion cues, have been widely used in studies of visual perception [6][7][8]5,9]; human motion tracking and activity recognition in computer vision [10][11][12][13][14][15]; clinical gait analysis and sports science research [16][17][18][19][20]; character animation [21,22]; augmented reality and virtual reality [14]. Motion analysis from reduced MLDs allows us to use quantitative, concise and accurate data to investigate essential recognition features in visual perception, motion modelling, kinematic formulation and motion synthesis.…”

Section: Introductionmentioning

confidence: 99%

Recognition of human periodic movements from unstructured information using a motion-based frequency domain approach

Meng

Holstein

2006

Image and Vision Computing

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Recognition of Human Periodic Movements From UnstructuredInformation Using A Motion-based Frequency Domain Approach Feature-based motion cues play important role in biological visual perception. We present a motion-based frequency-domain scheme for human periodic motion recognition. As a baseline study of feature-based recognition we use unstructured feature-point kinematic data obtained directly from a marker-based optical motion capture (MoCap) system, rather than accommodate bootstrapping from the low-level image processing of feature detection. Motion power spectral analysis is applied to a set of unidentified trajectories of feature points representing whole body kinematics. Feature power vectors are extracted from motion power spectra and mapped to a low dimensionality of feature space as motion templates that offer frequency domain signatures to characterise different periodic motions. Recognition of a new instance of periodic motion against pre-stored motion templates is carried out by seeking best motion power spectral similarity. We test this method through nine examples of human periodic motion using MoCap data. The recognition results demonstrate that feature-based spectral analysis allows classification of periodic motions from low-level, un-structured interpretation without recovering underlying kinematics. Contrasting with common structure-based spatio-temporal approaches, this motion-based frequency-domain method avoids a time-consuming recovery of underlying kinematic structures in visual analysis and largely reduces the parameter domain in the presence of human motion irregularities.

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The measurement of human motion: A comparison of commercially available systems

Cited by 310 publications

References 5 publications

A comparison of the upper limb movement kinematics utilized by children playing virtual and real table tennis

A comparison of the upper limb movement kinematics utilized by children playing virtual and real table tennis

An investigation into multi-dimensional prediction models to estimate the pose error of a quadcopter in a CSP plant setting

Recognition of human periodic movements from unstructured information using a motion-based frequency domain approach

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