MoCap Data Segmentation and Classification Using Kernel Based Multi-channel Analysis

García-Vega, Sergio; Álvarez-Meza, Andrés Marino; Castellanos-Domínguez, G.

doi:10.1007/978-3-642-41827-3_62

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…Now, DB approaches reside in the construction of a dissimilarity space from the input time series, which are later used to train a classifier, e.g., a K-nearest neighbors [ 33 , 34 ]. In general, the Euclidean distance (ED) is the most straightforward DB approach.…”

Section: Introductionmentioning

confidence: 99%

“…Even though the classification performance is reasonable, exhaustive training is required, the overfitting issue arises for small databases, and the provided algorithms often lack straightforward interpretability [32]. Now, DB approaches reside in the construction of a dissimilarity space from the input time series, which are later used to train a classifier, e.g., a K-nearest neighbors [33,34]. In general, the Euclidean distance (ED) is the most straightforward DB approach.…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability

Valencia-Marin

Pulgarín-Giraldo

Velásquez-Martínez

et al. 2021

Sensors

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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).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability

Valencia-Marin

Pulgarín-Giraldo

Velásquez-Martínez

et al. 2021

Sensors

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show abstract

“…In other words, motion segmentation requires action/gesture recognition. Pertinent methods have been proposed for visual methods and MoCap, e.g., [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An Approach to the Creation and Presentation of Reference Gesture Datasets, for the Preservation of Traditional Crafts

et al. 2020

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A wide spectrum of digital data are becoming available to researchers and industries interested in the recording, documentation, recognition, and reproduction of human activities. In this work, we propose an approach for understanding and articulating human motion recordings into multimodal datasets and VR demonstrations of actions and activities relevant to traditional crafts. To implement the proposed approach, we introduce Animation Studio (AnimIO) that enables visualisation, editing, and semantic annotation of pertinent data. AnimIO is compatible with recordings acquired by Motion Capture (MoCap) and Computer Vision. Using AnimIO, the operator can isolate segments from multiple synchronous recordings and export them in multimodal animation files. AnimIO can be used to isolate motion segments that refer to individual craft actions, as described by practitioners. The proposed approach has been iteratively designed for use by non-experts in the domain of 3D motion digitisation.

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A Similarity Indicator for Differentiating Kinematic Performance Between Qualified Tennis Players

Pulgarín-Giraldo

Álvarez-Meza

Melo-Betancourt

et al. 2017

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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MoCap Data Segmentation and Classification Using Kernel Based Multi-channel Analysis

Cited by 4 publications

References 4 publications

An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability

An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability

An Approach to the Creation and Presentation of Reference Gesture Datasets, for the Preservation of Traditional Crafts

A Similarity Indicator for Differentiating Kinematic Performance Between Qualified Tennis Players

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