title: A data mining technique for discovering distinct patterns of hand signs: implications in user training and computer interface design.
abstract: Hand signs are considered as one of the important ways to enter information into computers for certain tasks. Computers receive sensor data of hand signs for recognition. When using hand signs as computer inputs, we need to (1) train computer users in the sign language so that their hand signs can be easily recognized by computers, and (2) design the computer interface to avoid the use of confusing signs for improving user input performance and user satisfaction. For user training and computer interface design, it is important to have a knowledge of which signs can be easily recognized by computers and which signs are not distinguishable by computers. This paper presents a data mining technique to discover distinct patterns of hand signs from sensor data. Based on these patterns, we derive a group of indistinguishable signs by computers. Such information can in turn assist in user training and computer interface design.
authors.affiliation: Department of Industrial Engineering, Arizona State University, Box 875906, Tempe, Arizona 85287-5906, USA. nongye@asu.edu
authors.forename: Nong
authors.initials: N
authors.lastname: Ye
authors.forename: Xiangyang
authors.initials: X
authors.lastname: Li
authors.forename: Toni
authors.initials: T
authors.lastname: Farley
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D006804:Ergonomics; D006801:Humans; D008369:Manual Communication; D008432:Mathematical Computing; D015233:Models, Statistical; D010364:Pattern Recognition, Visual; D012813:Sign Language; D014584:User-Computer Interface