Comparison of lower limb and trunk kinematics between markerless and marker-based motion capture systems

Perrott, M.; Pizzari, Tania; Cook, Jill; McClelland, Jodie A.

doi:10.1016/j.gaitpost.2016.10.020

Cited by 36 publications

(38 citation statements)

References 12 publications

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“…Xsens MVN [75]), and can already provide GRF and joint moment estimates during stereotypical activities such as walking [76,77]. To overcome discomfort and movement restriction issues associated with the use of multiple body-worn devices, markerless motion capture techniques are a non-invasive method for measuring different biomechanical variables in various sport environments [78,79,80,81,82,83]. These techniques may in the future allow for load metrics to be estimated at different levels.…”

Section: From Lab To Fieldmentioning

confidence: 99%

Measuring biomechanical loads in team sports – from lab to field

Verheul

Nedergaard

Vanrenterghem

et al. 2020

Science and Medicine in Football

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The benefits of differentiating between the physiological and biomechanical load-response pathways in football and other (team) sports have become increasingly recognised. In contrast to physiological loads however, the biomechanical demands of training and competition are still not well understood, primarily due to the difficulty of quantifying biomechanical loads in a field environment. Although musculoskeletal adaptation and injury are known to occur at a tissue level, several biomechanical load metrics are available that quantify loads experienced by the body as a whole, its different structures and the individual tissues that are part of these structures. This paper discusses the distinct aspects and challenges that are associated with measuring biomechanical loads at these different levels in laboratory and/or field contexts. Our hope is that through this paper, sport scientists and practitioners will be able to critically consider the value and limitations of biomechanical load metrics and will keep pursuing new methods to measure these loads within and outside the lab, as a detailed load quantification is essential to better understand the biomechanical load-response pathways that occur in the field.

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Section: From Lab To Fieldmentioning

confidence: 99%

Measuring biomechanical loads in team sports – from lab to field

Verheul

Nedergaard

Vanrenterghem

et al. 2020

Science and Medicine in Football

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“…In [14], a comparison of marker-less and marker-based motion capture technologies through simultaneous data collection during gait has been presented. A recently conducted study [56] showed that a marker-based and a marker-less systems have similar ranges of variation in the angle from the start of a squat to peak squat in the pelvis and lower limb in a single leg squat. Only some geometry information on subjects could be reconstructed aided by some calibration equipment (four calibration taps were placed to support reconstruction of geometry information) Table 1 contains a collective summary of major databases for gait recognition, which were evoked in the relevant literature.…”

Section: Background and Relevant Workmentioning

confidence: 99%

Calibrated and synchronized multi-view video and motion capture dataset for evaluation of gait recognition

Kwolek

Michalczuk

Krzeszowski

et al. 2019

Multimed Tools Appl

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We introduce synchronized and calibrated multi-view video and motion capture dataset for motion analysis and gait identification. The 3D gait dataset consists of 166 data sequences with 32 people. In 128 data sequences, each of 32 individuals was dressed in his/her clothes, in 24 data sequences, 6 of 32 performers changed clothes, and in 14 data sequences, 7 of the performers had a backpack on his/her back. In a single recording session, every performer walked from right to left, then from left to right, and afterwards on the diagonal from upperright to bottom-left and from bottom-left to upper-right corner of a rectangular scene. We demonstrate that a baseline algorithm achieves promising results in a challenging scenario, in which gallery/training data were collected in walks perpendicular/facing to the cameras, whereas the probe/testing data were collected in diagonal walks. We compare performances of biometric gait recognition that were achieved on marker-less and marker-based 3D data. We present recognition performances, which were achieved by a convolutional neural network and classic classifiers operating on gait signatures obtained by multilinear principal component analysis. The availability of synchronized multi-view image sequences with 3D locations of body markers creates a number of possibilities for extraction of discriminative gait signatures. The gait data are available at http://bytom.pja.edu.pl/projekty/hm-gpjatk/.

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“…Another study provides evidence that a marker-based and a markerless protocol report similar ranges of pelvis and lower limb angles in the single-leg squat [22]. The knee's range of motion was under special consideration because of the frequency of injuries in sport activities.…”

Section: Forward Stepmentioning

confidence: 99%

Repeatability of the three dimensional kinematics of the pelvis, spine and lower limbs while performing selected exercises

Skublewska‐Paszkowska

Lukasik

Smółka

et al. 2017

ITM Web Conf.

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Abstract. Three-dimensional optical systems are used for calculating many kinetic and kinematic parameters. The obtained data are precise; however, their repeatability is a very important aspect. The aim of this paper is to verify the range of motion repeatability of one healthy subject in the joints of the pelvis, spine and lower limbs based on the coefficient of variation. The participant performed seven exercises, repeated five times: two-leg squat, single-leg squat, forward bending, forward-step motion, step onto the stair, hip extension in a standing position and tip-toe extension while standing. Motion was recorded using Vicon motion capture system consisting of eight NIR cameras. The participant had 39 markers attached to her body according to the Plug-in Gait model. The coefficient of variation was calculated in three dimensions (X, Y and Z). The greatest repeatability, pursuant to the coefficient, was observed during the two-leg squat and forward bending in the sagittal plane (X coordinate). It was also high during the singleleg squat. The lowest repeatability was observed during the tip-toe extension while standing and the hip extension in a standing position. During the step onto the stair and the forward-step motion, a higher repeatability of measurement occurred in the open kinematic chain than in the closed chain; in the hip extension the reverse occurred. Repeatability of a range of motion is different in two types of kinematic chain and in 7 exercises. Exercises such as tip-toe extension and hip extension, which require a greater ability to balance, indicated more variability in movement.

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Comparison of lower limb and trunk kinematics between markerless and marker-based motion capture systems

Cited by 36 publications

References 12 publications

Measuring biomechanical loads in team sports – from lab to field

Measuring biomechanical loads in team sports – from lab to field

Calibrated and synchronized multi-view video and motion capture dataset for evaluation of gait recognition

Repeatability of the three dimensional kinematics of the pelvis, spine and lower limbs while performing selected exercises

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