A Method for Lifelong Gesture Learning Based on Growing Neural Gas

Autor:	Keith Evan Green, Jessica Merino, Anthony L. Threatt, Johnell O. Brooks, Joe Manganelli, Ian D. Walker, Paul M. Yanik
Rok vydání:	2014
Předmět:	Neural gas Ubiquitous computing Human–computer interaction Computer science business.industry Gesture recognition Command language SIGNAL (programming language) Lifelong learning Artificial intelligence business Human–robot interaction Gesture
Zdroj:	Human-Computer Interaction. Advanced Interaction Modalities and Techniques ISBN: 9783319072296 HCI (2)
DOI:	10.1007/978-3-319-07230-2_19
Popis:	Gesture-based interfaces offer the possibility of an intuitive command language for assistive robotics and ubiquitous computing. As an individual’s health changes with age, their ability to consistently perform standard gestures may decrease, particularly towards the end of life. Thus, such interfaces will need to be capable of learning commands which are not choreographed ahead of time by the system designers. This circumstance illustrates the need for a system which engages in lifelong learning and is capable of discerning new gestures and the user’s desired response to them. This paper describes an innovative approach to lifelong learning based on clustered gesture representations identified through the Growing Neural Gas algorithm. The simulated approach utilizes a user-generated reward signal to progressively refine the response of an assistive robot toward a preferred goal configuration.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::254a4434e6ac912716c8fb45d1f7fe82 https://doi.org/10.1007/978-3-319-07230-2_19 Zobrazit plný text záznamu