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

Félix Bigand¹, Elise Prigent¹, Bastien Berret²

¹Université Paris-Saclay, CNRS, LISN, Orsay, France.

|October 29, 2021

Summary

Principal Component Analysis (PCA) effectively models sign language (SL) movements by identifying key components. This approach simplifies complex body motions for efficient analysis and recognition of SL.

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Area of Science:

Computational linguistics
Biomechanical analysis
Human-computer interaction

Background:

Sign Language (SL) involves complex, continuous body movements, posing challenges for computational analysis.
Developing efficient models for describing and analyzing SL movements is crucial for technological applications.

Purpose of the Study:

To investigate the application of Principal Component Analysis (PCA) for decomposing and analyzing upper-body movements in French Sign Language (FSL).
To determine if a reduced set of elementary movements (principal movements or PMs) can efficiently represent continuous SL discourse.

Main Methods:

Applied PCA to upper-body motion capture data from six signers producing continuous FSL.
Extracted common PMs across all signers and individual PMs for each signer.
Analyzed the variance explained by the extracted PMs and the similarity of PM subspaces.

Main Results:

The first eight common PMs captured 94.6% of the movement variance, even without time synchronization.
The number of PMs needed to explain 94.6% of the variance was consistent across common and individual analyses.
PM subspaces demonstrated high similarity among different signers.

Conclusions:

Continuous upper-body motion in unconstrained SL discourse can be effectively represented by a limited set of elementary movements.
This finding supports the development of efficient automatic SL processing tools, including recognition and generation systems, using motion capture data.