Paper Info
Title
An Elicitation Study on Gesture Preferences and Memorability Toward a Practical Hand-Gesture Vocabulary for Smart Televisions
Abstract
With the introduction of new depth-sensing technologies, interactive hand-gesture devices (such as smart televisions and displays) have been rapidly emerging. However, given the lack of a common vocabulary, most hand-gesture control commands are device-specific, burdening the user into learning different vocabularies for different devices. In order for hand gestures to become a natural communication for users with interactive devices, a standardized interactive hand-gesture vocabulary is necessary. Recently, researchers have approached this issue by conducting studies that elicit gesture vocabularies based on users' preferences. Nonetheless, a universal vocabulary has yet to be proposed. In this paper, a thorough design methodology for achieving such a universal hand-gesture vocabulary is presented. The methodology is derived from the work of Wobbrock et al. and includes four steps: 1) a preliminary survey eliciting users' attitudes; 2) a broader user survey in order to construct the universal vocabulary via results of the preliminary survey; 3) an evaluation test to study the implementation of the vocabulary; and 4) a memory test to analyze the memorability of the vocabulary. The proposed vocabulary emerged from this methodology achieves an agreement score exceeding those of the existing studies. Moreover, the results of the memory test show that, within a 15-min training session, the average accuracy of the proposed vocabulary is 90.71%. Despite the size of the proposed gesture vocabulary being smaller than that of similar work, it shares the same functionality, is easier to remember and can be integrated with smart TVs, interactive digital displays, and so on.
Year
DOI
Venue
2015
10.1109/ACCESS.2015.2432679
IEEE Access
Keywords
Field
DocType
Hand-gesture interaction,gesture elicitation study,preferences and attitudes,gesture set,human-computer interaction
Computer science,Gesture,Gesture recognition,Design methods,Behavioural sciences,Vocabulary,Multimedia
Journal
Volume
ISSN
Citations 
3
2169-3536
9
PageRank 
References 
Authors
0.48
18
4
Name
Order
Citations
PageRank
Haiwei Dong112217.60
Ali Danesh290.82
Nadia Figueroa3488.64
El Saddik, A.465055.74