An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability.
Cristian Kaori Valencia-Marin,Juan D. Pulgarin-Giraldo,Luisa Fernanda Velásquez-Martínez,Andrés Marino Álvarez-Meza,Germán Castellanos-Domínguez +4 more
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In this paper, an enhanced Hilbert embedding-based approach from a cross-covariance operator, termed EHECCO, was introduced to map the input Mocap time series to a tensor space built from both 3D skeletal joints and a principal component analysis-based projection.Abstract:
Motion capture (Mocap) data are widely used as time series to study human movement. Indeed, animation movies, video games, and biomechanical systems for rehabilitation are significant applications related to Mocap data. However, classifying multi-channel time series from Mocap requires coding the intrinsic dependencies (even nonlinear relationships) between human body joints. Furthermore, the same human action may have variations because the individual alters their movement and therefore the inter/intraclass variability. Here, we introduce an enhanced Hilbert embedding-based approach from a cross-covariance operator, termed EHECCO, to map the input Mocap time series to a tensor space built from both 3D skeletal joints and a principal component analysis-based projection. Obtained results demonstrate how EHECCO represents and discriminates joint probability distributions as kernel-based evaluation of input time series within a tensor reproducing kernel Hilbert space (RKHS). Our approach achieves competitive classification results for style/subject and action recognition tasks on well-known publicly available databases. Moreover, EHECCO favors the interpretation of relevant anthropometric variables correlated with players’ expertise and acted movement on a Tennis-Mocap database (also publicly available with this work). Thereby, our EHECCO-based framework provides a unified representation (through the tensor RKHS) of the Mocap time series to compute linear correlations between a coded metric from joint distributions and player properties, i.e., age, body measurements, and sport movement (action class).read more
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A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson's Disease.
Samuel Rupprechter,Gareth Morinan,Yuwei Peng,Thomas Foltynie,Krista G. Sibley,Rimona S. Weil,Louise-Ann Leyland,Fahd Baig,Francesca Morgante,Roee Gilron,Robert Wilt,Philip A. Starr,Robert A. Hauser,Jonathan O’Keeffe +13 more
TL;DR: In this paper, the Gamma-Poisson Bayesian model was used to estimate the severity of gait impairment in Parkinson's disease using a computer vision-based methodology, which can be used to obtain an estimate for a rating to catch potential errors or to gain an initial rating in the absence of a trained clinician.
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Andrea Zanela,Tommaso Schirinzi,Nicola Biagio Mercuri,Alessandro Stefani,Cristian Romagnoli,Giuseppe Annino,Vincenzo Bonaiuto,Rocco Cerroni +7 more
TL;DR: In this article , the authors evaluated whether artificial intelligence techniques can be used to objectively assess gait impairment in subjects with Parkinson's disease, and the results of a cohort of ten subjects, five with a Parkinson disease diagnosis at different degrees of severity.
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