Intent Recognition Using Neural Networks and Kalman Filters

Autor:	Gökçen Aslan Aydemir, Pradipta Biswas, Patrick Langdon, Simon J. Godsill
Rok vydání:	2013
Předmět:	Artificial neural network business.industry Time delay neural network Computer science Significant part Motor impairment Kalman filter Machine learning computer.software_genre Artificial intelligence Intent recognition business computer Graphical user interface
Zdroj:	Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data ISBN: 9783642391453 CHI-KDD
DOI:	10.1007/978-3-642-39146-0_11
Popis:	Pointing tasks form a significant part of human-computer interaction in graphical user interfaces. Researchers tried to reduce overall pointing time by guessing the intended target a priori from pointer movement characteristics. The task presents challenges due to variability of pointer movements among users and also diversity of applications and target characteristics. Users with age-related or physical impairment makes the task more challenging due to there variable interaction patterns. This paper proposes a set of new models for predicting intended target considering users with and without motor impairment. It also sets up a set of evaluation metrics to compare those models and finally discusses the utilities of those models. Overall we achieved more than 63% accuracy of target prediction in a standard multiple distractor task while our model can recognize the correct target before the user spent 70% of total pointing time, indicating a 30% reduction of pointing time in 63% pointing tasks.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::edb247139755bfbcba2ec086bedf5208 https://doi.org/10.1007/978-3-642-39146-0_11 Zobrazit plný text záznamu