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SOMA: Solving Optical Marker-Based MoCap Automatically
[article]
2021
arXiv
pre-print
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Marker-based optical motion capture (mocap) is the "gold standard" method for acquiring accurate 3D human motion in computer vision, medicine, and graphics. ...
To enable learning, we generate massive training sets of simulated noisy and ground truth mocap markers animated by 3D bodies from AMASS. ...
Introduction Marker-based optical motion capture (mocap) systems record 2D infrared images of light reflected or emitted by a set of markers placed at key locations on the surface of a subject's body. ...
arXiv:2110.04431v1
fatcat:ow5af3etmjcxvh2l2qt26uvgaq
Development and Validation of a Deep Learning Algorithm and Open-Source Platform for the Automatic Labelling of Motion Capture Markers
2021
IEEE Access
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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. ...
INDEX TERMS Optical motion capture, marker labelling, machine learning, biomechanics. 36444 This work is licensed under a Creative Commons Attribution 4.0 License. ...
INTRODUCTION Optical motion capture has been widely used for entertainment, clinical, and research applications to quantify human motion. ...
doi:10.1109/access.2021.3062748
fatcat:kg46za644zf5jlfmezdjmm36sa
Development and validation of a deep learning algorithm and open-source platform for the automatic labelling of motion capture markers
[article]
2021
bioRxiv
pre-print
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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. ...
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 ...
Our aim was to develop an open-source algorithm that can automatically label motion capture markers using machine learning in the presence of occluded and extraneous markers. ...
doi:10.1101/2021.02.08.429993
fatcat:fysqcnmbczb7xlfjmqwzwl4xje
What Does a Hand-Over Tell?—Individuality of Short Motion Sequences
2019
Biomimetics
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We study this question by selecting a set of individual participant characteristics and analysingmotion captured trajectories of an exemplary class of familiar movements, namely handover of anobject to ...
How much information with regard to identity and further individual participantcharacteristics are revealed by relatively short spatio-temporal motion trajectories of a person? ...
Abbreviations The following abbreviations are used in this manuscript: MANOVA multivariate analyses of variance RANOVA repeated measures analyses of variance ...
doi:10.3390/biomimetics4030055
pmid:31394826
pmcid:PMC6784304
fatcat:r6k5ge6agbfpvnxelbrhdxbese
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
[article]
2020
bioRxiv
pre-print
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In this work, we improve on these methods (particularly in the regime of few training labels) by leveraging the rich spatiotemporal structures pervasive in behavioral video --- specifically, the spatial ...
Noninvasive behavioral tracking of animals is crucial for many scientific investigations. Recent transfer learning approaches for behavioral tracking have considerably advanced the state of the art. ...
Acknowledgments and Disclosure of Funding We thank the authors of DeepLabCut [4] for generously sharing their code and data. This work was supported by grants from the Wellcome Trust ...
doi:10.1101/2020.08.20.259705
fatcat:ytw7f4thq5cfrbv2ia6glccvu4
The MAHNOB Mimicry Database: A database of naturalistic human interactions
2015
Pattern Recognition Letters
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The best reported results are session-dependent, and affected by the sparsity of positive examples in the data. ...
To provide a benchmark for efforts in machine understanding of mimicry behaviour, we report a number of baseline experiments based on visual data only. ...
They used motion energy, optical flow, and prosodic features, and calculated cross-correlation and magnitude coherence between all pairs of features, in one-second windows. ...
doi:10.1016/j.patrec.2015.03.005
fatcat:wd2vg6acyffkhcn2w3duftxngu
Neural Scene Decomposition for Multi-Person Motion Capture
[article]
2019
arXiv
pre-print
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However, when it comes to 3D motion capture of multiple people, these features are only of limited use. ...
limited labeled data. ...
This work was supported by the Swiss National Science Foundation and a Microsoft JRC Project. ...
arXiv:1903.05684v1
fatcat:emq6hahrarbhtdqkeu5gyrvhrm
Neural Scene Decomposition for Multi-Person Motion Capture
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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However, when it comes to 3D motion capture of multiple people, these features are only of limited use. ...
limited labeled data. ...
This work was supported by the Swiss Innovation Agency and by a Microsoft Joint Research Project. ...
doi:10.1109/cvpr.2019.00789
dblp:conf/cvpr/RhodinCKSF19
fatcat:i6fuz4c5jnartoe6dp5zzkdbum
Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2012.09237v1 fatcat:52kj24sdebaivajaahidkbfp6yOther Versions
A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement. ...
State-of-the-art instrumented movement analysis is time- and cost-intensive, since it requires placing physical or virtual markers. ...
in every frame of a video in order to capture the fine-grained details of the behavior. ...
arXiv:2012.09237v3
fatcat:yiz7fmblefdrzlm2k7orjr3oga
Representation, Analysis, and Recognition of 3D Humans
2018
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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An increasing importance is also assumed by learning solutions, where hand-crafted descriptors are substituted by deep features that are learned directly from the data. ...
way; • volumetric: the volume delimited by the shape surface is accounted for by the representation; • topological: topological variations of the shape are captured by the representation; • landmarks ...
Applications were shown in modalities as diverse as marker-based motion capture, RGBD sensors, and activity quality assessment for applications in stroke rehabilitation. Covariance Matrix (CM). ...
doi:10.1145/3182179
fatcat:ds55t4md2na2tibtyg4llerf3q
Latent Variable Algorithms for Multimodal Learning and Sensor Fusion
[article]
2019
arXiv
pre-print
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A gating modular neural network dynamically generates a set of mixing weights for outputs from sensor networks by balancing utility of all sensors' information. ...
Multimodal learning has been lacking principled ways of combining information from different modalities and learning a low-dimensional manifold of meaningful representations. ...
Acknowledgement Part of this work is based on Lijiang Guo's PhD qualify exam paper. We would like to thank Dr. Geoffrey Fox, Dr. Minje Kim, Dr. Francesco Nesta, Dr. Michael Ryoo and Dr. ...
arXiv:1904.10450v1
fatcat:6634ghs74fcd3fz3l4nov4rb3m
Cellular Level Brain Imaging in Behaving Mammals: An Engineering Approach
2015
Neuron
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Fluorescence imaging offers expanding capabilities for recording neural dynamics in behaving mammals, including the means to monitor hundreds of cells targeted by genetic type or connectivity, track cells ...
We discuss recent progress and future directions for imaging in behaving mammals from a systems engineering perspective, which seeks holistic consideration of fluorescent indicators, optical instrumentation ...
Notably, image analysis by ICA is well known in multiple contexts to be robust to modest levels of motion artifact and violations of the assumption of independence (McKeown et al., 1998; Mukamel et al ...
doi:10.1016/j.neuron.2015.03.055
pmid:25856491
pmcid:PMC5758309
fatcat:4mllguw6fzbl3okx4y5lxdiiqe
Machine Learning on Human Connectome Data from MRI
[article]
2016
arXiv
pre-print
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Recently, researchers have been exploring the application of machine learning models to connectome data in order to predict clinical outcomes and analyze the importance of subnetworks in the brain. ...
Finally, we conclude by summarizing the current state of the art and by outlining what we believe are strategic directions for future research. ...
First, a null distribution of the frequency selection of each feature is created by training a random forest on data with permuted class labels. ...
arXiv:1611.08699v1
fatcat:opmtmr3eejbjjm4swfmg54g4q4
Chaos in magnetic flux ropes
2014
Plasma Physics and Controlled Fusion
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Conditional averaging is possible for only a number of rotation cycles as the field line motion becomes chaotic. ...
Each collision results in magnetic field line reconnection and the generation of a quasi-separatrix layer. ...
The algorithm splits the data up in vectors of length n, wherein each sample of the vector is separated by a lag J , and J is chosen as the lag at which the auto-correlation of the time trace drops below ...
doi:10.1088/0741-3335/56/6/064002
fatcat:dz6wywr4s5aorlt2fmu3sqf2m4
In-Ear Accelerometer-Based Sensor for Gait Classification
2020
IEEE Sensors Journal
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For several years, the detection of gait has been popularly implemented using wearable sensors, especially in the sports and medical areas. ...
The purpose of this paper is to demonstrate the accuracy and reliability of in-ear accelerometer sensor to perform gait classification, between the activities walking and running. ...
ACKNOWLEDGMENT The authors would like to thank all the students who participated in this study. ...
doi:10.1109/jsen.2020.3002589
fatcat:oid4iyvxzvar5kremi72y7vbby
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