Auto-labelling of Markers in Optical Motion Capture by Permutation Learning

Ghorbani, Saeed; Etemad, Ali; Troje, Nikolaus F.

doi:10.1007/978-3-030-22514-8_14

Cited by 21 publications

(32 citation statements)

References 17 publications

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“…On the other hand, IMU accelerometers measure a combination of the gravitational and translational accelerations. As reported by Woodman [ 21 ], it is necessary to have very accurate rotation sensors in inertial navigation systems, because knowing the precise orientation of the body allows to properly subtract the gravitational acceleration from the measurement, in order to find the translational acceleration. Each IMU provides its acceleration expressed in its local reference frame (previously denoted by I).…”

Section: Methodsmentioning

confidence: 99%

“…The problem with optical motion capture systems is that it is very difficult to ensure that all markers are visible to the cameras all the time and, moreover, other reflective objects present in the capture zone can be incorrectly identified as markers. In general, obtaining the skeletal motion involves some manual post-processing of the captured data, so the technique is not straightforward [ 20 , 21 ]. This problem can be overcome by using an extended Kalman filter (EKF) [ 22 ], as will be described later in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis

Cuadrado

Michaud

Lugrís

et al. 2021

Sensors

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Optical motion capture is currently the most popular method for acquiring motion data in biomechanical applications. However, it presents a number of problems that make the process difficult and inefficient, such as marker occlusions and unwanted reflections. In addition, the obtained trajectories must be numerically differentiated twice in time in order to get the accelerations. Since the trajectories are normally noisy, they need to be filtered first, and the selection of the optimal amount of filtering is not trivial. In this work, an extended Kalman filter (EKF) that manages marker occlusions and undesired reflections in a robust way is presented. A preliminary test with inertial measurement units (IMUs) is carried out to determine their local reference frames. Then, the gait analysis of a healthy subject is performed using optical markers and IMUs simultaneously. The filtering parameters used in the optical motion capture process are tuned in order to achieve good correlation between the obtained accelerations and those measured by the IMUs. The results show that the EKF provides a robust and efficient method for optical system-based motion analysis, and that the availability of accelerations measured by inertial sensors can be very helpful for the adjustment of the filters.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis

Cuadrado

Michaud

Lugrís

et al. 2021

Sensors

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show abstract

“…Optical capture with marker: Applying MLD previously summarized and categorized a traditional optical capture technique using marker. Attaching markers to the actor's body to have the 3D anatomic human body is used for skeleton visibility assessment (fitting), optical markers, motion capture, or mapping motion onto skeleton [53][54][55][56][57]. More advanced cameras helped on performing such analyses (i.e., Pan-Tilt Camera Tracking [54]).…”

Section: Motion Capturementioning

confidence: 99%

“…Some of the approaches here were not necessarily used for recognition of human action but applied for augmented reality or graphic applications [58,59], despite of employing deep network that can be justified as a biological-inspired model [60]. However, some of trajectory labelling applying permutation learning (using deep learning) could be more relevant [57].…”

Section: Motion Capturementioning

confidence: 99%

Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

Yousefi

Loo

2019

Electronics

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Theoretical neuroscience investigation shows valuable information on the mechanism for recognizing the biological movements in the mammalian visual system. This involves many different fields of researches such as psychological, neurophysiology, neuro-psychological, computer vision, and artificial intelligence (AI). The research on these areas provided massive information and plausible computational models. Here, a review on this subject is presented. This paper describes different perspective to look at this task including action perception, computational and knowledge based modeling, psychological, and neuroscience approaches.

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“…Han et al (2018) and Rosskamp et al (2020) rendered the 3D marker coordinates as a depth image and used convolutional neural networks to perform the marker labelling. Ghorbani et al (2019) framed the problem as the recovery of the correct ranking of a shuffled marker set that could be approached using permutation learning. Jiménez Bascones et al (2019) used the Adaboost algorithm to select an optimal set of weak classifiers based on geometric relationships between markers to assign marker labels.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Deep Learning Algorithm and Open-Source Platform for the Automatic Labelling of Motion Capture Markers

Clouthier

Ross

Mavor

et al. 2021

Preprint

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The purpose of this work was to develop an open-source deep learning-based algorithm for motion capture marker labelling that can be trained on measured or simulated marker trajectories. In the proposed algorithm, a deep neural network including recurrent layers is trained on measured or simulated marker trajectories. Labels are assigned to markers using the Hungarian algorithm and a predefined generic marker set is used to identify and correct mislabeled markers. The algorithm was first trained and tested on measured motion capture data. Then, the algorithm was trained on simulated trajectories and tested on data that included movements not contained in the simulated data set. The ability to improve accuracy using transfer learning to update the neural network weights based on labelled motion capture data was assessed. The effect of occluded and extraneous markers on labelling accuracy was also examined. Labelling accuracy was 99.6% when trained on measured data and 92.8% when trained on simulated trajectories, but could be improved to up to 98.8% through transfer learning. Missing or extraneous markers reduced labelling accuracy, but results were comparable to commercial software. The proposed labelling algorithm can be used to accurately label motion capture data in the presence of missing and extraneous markers and accuracy can be improved as data are collected, labelled, and added to the training set. The algorithm and user interface can reduce the time and manual effort required to label optical motion capture data, particularly for those with limited access to commercial software.

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Auto-labelling of Markers in Optical Motion Capture by Permutation Learning

Cited by 21 publications

References 17 publications

Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis

Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis

Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

Development and Validation of a Deep Learning Algorithm and Open-Source Platform for the Automatic Labelling of Motion Capture Markers

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