scholarly journals Decomposing spontaneous sign language into elementary movements: A principal component analysis-based approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259464
Author(s):  
Félix Bigand ◽  
Elise Prigent ◽  
Bastien Berret ◽  
Annelies Braffort
Keyword(s):  
Principal Component Analysis ◽  
Sign Language ◽  
Motion Capture ◽  
Computational Models ◽  
Body Movement ◽  
Principal Component ◽  
Component Analysis ◽  
Body Motion ◽  
Upper Body ◽  
Motion Capture Data

Sign Language (SL) is a continuous and complex stream of multiple body movement features. That raises the challenging issue of providing efficient computational models for the description and analysis of these movements. In the present paper, we used Principal Component Analysis (PCA) to decompose SL motion into elementary movements called principal movements (PMs). PCA was applied to the upper-body motion capture data of six different signers freely producing discourses in French Sign Language. Common PMs were extracted from the whole dataset containing all signers, while individual PMs were extracted separately from the data of individual signers. This study provides three main findings: (1) although the data were not synchronized in time across signers and discourses, the first eight common PMs contained 94.6% of the variance of the movements; (2) the number of PMs that represented 94.6% of the variance was nearly the same for individual as for common PMs; (3) the PM subspaces were highly similar across signers. These results suggest that upper-body motion in unconstrained continuous SL discourses can be described through the dynamic combination of a reduced number of elementary movements. This opens up promising perspectives toward providing efficient automatic SL processing tools based on heavy mocap datasets, in particular for automatic recognition and generation.

scholarly journals Sign Language Recognition using Principal Component Analysis and Support Vector Machine

2021 ◽  
Vol 4 (01) ◽  
pp. 49
Author(s):  
Astri Novianty ◽  
Fairuz Azmi
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Sign Language ◽  
Principal Component ◽  
Histogram Equalization ◽  
Component Analysis ◽  
World Health ◽  
Support Vector ◽  
Language Recognition ◽  
Sign Language Recognition

The World Health Organization (WHO) estimates that over five percent of the world's population are hearing-impaired. One of the communication problems that often arise between deaf or speech impaired with normal people is the low level of knowledge and understanding of the deaf or speech impaired's normal sign language in their daily communication. To overcome this problem, we build a sign language recognition system, especially for the Indonesian language. The sign language system for Bahasa Indonesia, called Bisindo, is unique from the others. Our work utilizes two image processing algorithms for the pre-processing, namely the grayscale conversion and the histogram equalization. Subsequently, the principal component analysis (PCA) is employed for dimensional reduction and feature extraction. Finally, the support vector machine (SVM) is applied as the classifier. Results indicate that the use of the histogram equalization significantly enhances the accuracy of the recognition. Comprehensive experiments by applying different random seeds for testing data confirm that our method achieves 76.8% accuracy. Accordingly, a more robust method is still open to enhance the accuracy in sign language recognition.

Quantification of Upper-Body Synergies: A Case Comparison for Stroke and Non-Stroke Victims

2016 ◽  
Author(s):  
Omid Heidari ◽  
John O. Roylance ◽  
Alba Perez-Gracia ◽  
Eydie Kendall
Keyword(s):  
Principal Component Analysis ◽  
Degrees Of Freedom ◽  
Principal Component ◽  
Component Analysis ◽  
Joint Space ◽  
Upper Body ◽  
Dimensional Synthesis ◽  
Revolute Joints ◽  
Case Comparison ◽  
The Individual

Motion synergies are principal components of the movement, obtained as combinations of joint degrees of freedom, that account for common postures of the human body. These synergies are usually obtained by capturing the motion of the human joints and reducing the dimensionality of the joint space with techniques such as principal component analysis. In this work, an experimental procedure to investigate the synergies of the upper body is developed and the results of the pilot study are shown. The upper-limb kinematics includes the joint complexes of the hand, wrist, forearm, elbow, and shoulder. The different kinematic models in the literature have been reviewed, and a serial chain is considered from the upper arm. A three degree of freedom (3-DOF) linkage containing two revolute joints and one prismatic joint has been chosen to simulate the shoulder motion. A spherical joint represents the Glenohumeral (GH) joint; the elbow and ulna-radius rotations are represented by two revolute joints and the wrist is modeled with two revolute joints. The hand has a tree structure and branches into the individual phalanges, with a 2-dof MCP joint and single R joints for the rest of the phalangeal joints. The data are collected using motion capture and the joint angles are calculated using a combination of dimensional synthesis and inverse kinematics. Principal component analysis can be used to extract the synergies for a set of previously-selected motions. The motions are performed by healthy subjects and subjects who have suffered stroke, in order to see the changes in the motion primitives. It is expected that this study will help quantify and classify some of the loss of motion due to stroke.

scholarly journals Face models based on a guided PCA of motion-capture data: speaker dependent variability in /s/-/R/ contrast production

2005 ◽  
Vol 40 ◽  
pp. 95-108
Author(s):  
Shinji Maeda
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Motion Capture ◽  
Principal Component ◽  
Acoustic Properties ◽  
Motion Capture Data ◽  
Motion Capture System ◽  
Reasonable Accuracy ◽  
Speaker Variability ◽  
Coordinate Data

We measure face deformations during speech production using a motion capture system, which provides 3D coordinate data of about 60 markers glued on the speaker's face. An arbitrary orthogonal factor analysis followed by a principal component analysis (together called a guided PCA) of the data has showed that the first 6 factors explain about 90% of the variance, for each of our 3 speakers. The 6 derived factors, therefore, allow us to efficiently analyze or to reconstruct with a reasonable accuracy the observed face deformations. Since these factors can be interpreted in articulatory terms, they can reveal underlying articulatory organizations. The comparison of lip gestures in terms of data derived factors suggests that these speakers differently maneuver the lips to achieve contrast between /s/ and /R/. Such inter-speaker variability can occur because the acoustic contrast of these fricatives is shaped not only by the lip tube but also by cavities inside the mouth such as the sublingual cavity. In other words, these tube and cavity can acoustically compensate each other to produce their required acoustic properties.  

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