The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	A kinematic analysis of sign language
Author:	Koech, Chemuttaai C.
View Online:	njit-etd2007-007 (xii, 71 pages ~ 6.9 MB pdf)
Department:	Department of Biomedical Engineering
Degree:	Master of Science
Program:	Biomedical Engineering
Document Type:	Thesis
Advisory Committee:	Foulds, Richard A. (Committee chair) Adamovich, Sergei (Committee member) Mantilla, Bruno A. (Committee member)
Date:	2007-01
Keywords:	Kinematic analysis American Sign Language Oral language Translation
Availability:	Unrestricted
Abstract:	Signed languages develop among deaf populations and employ manual communication instead of voiced communication. Stokoe attributes classify individual signs in American Sign Language (ASL) and include handshape, hand location, movement, orientation, and facial expression. Signed and oral languages are not mutually understood, and many deaf individuals live in linguistic isolation. This research addresses computer translation between signing and speech, investigating sign duration in sentence context versus in isolation and identifying kinematic sign markers. To date, there has been little study of continuous signing kinematics; it was previously unknown if kinematic markers existed. Kinematic data were collected from a proficient signer with electromagnetic Flock of Birds® sensors (position! orientation of both wrists) and CyberGloves® (18 joint angles/ hand). The data were collected for each sign in isolation and in sentences. Mean sign duration decreased in sentence context due to coarticulation. There was evidence of finger joint and wrist velocity coordination, synchronicity and hand preshaping. Angular velocity maxima and minima indicated differentiation between handshapes. Minima in the wrists' tangential velocity signified Stokoe locations, and maxima indicated movement (sign midpoints or transition midpoints), which can serve as anchors in the segmentation process. These segmented locations and movements can be combined with handshape and wrist orientation to identify likely signs based on the kinematic database developed at NJIT.