AUTHOR=Bigand Félix , Prigent Elise , Berret Bastien , Braffort Annelies TITLE=Machine Learning of Motion Statistics Reveals the Kinematic Signature of the Identity of a Person in Sign Language JOURNAL=Frontiers in Bioengineering and Biotechnology VOLUME=9 YEAR=2021 URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2021.710132 DOI=10.3389/fbioe.2021.710132 ISSN=2296-4185 ABSTRACT=

Sign language (SL) motion contains information about the identity of a signer, as does voice for a speaker or gait for a walker. However, how such information is encoded in the movements of a person remains unclear. In the present study, a machine learning model was trained to extract the motion features allowing for the automatic identification of signers. A motion capture (mocap) system recorded six signers during the spontaneous production of French Sign Language (LSF) discourses. A principal component analysis (PCA) was applied to time-averaged statistics of the mocap data. A linear classifier then managed to identify the signers from a reduced set of principal components (PCs). The performance of the model was not affected when information about the size and shape of the signers were normalized. Posture normalization decreased the performance of the model, which nevertheless remained over five times superior to chance level. These findings demonstrate that the identity of a signer can be characterized by specific statistics of kinematic features, beyond information related to size, shape, and posture. This is a first step toward determining the motion descriptors necessary to account for the human ability to identify signers.