Prendinger, Helmut, Alvarez, Nahum, Sanchez-Ruiz, Antonio, Cavazza, Marc ORCID: https://orcid.org/0000-0001-6113-9696, Catarino, João, Oliveira, João, Prada, Rui, Fujimoto, Shuji and Shigematsu, Mika (2016) Intelligent biohazard training based on real-time task recognition. ACM Transactions on Interactive Intelligent Systems, 6 (3):21. pp. 1-32. ISSN 2160-6455 (Print), 2160-6463 (Online) (doi:10.1145/2883617)

Preview

PDF (Publisher's PDF) 19812 CAVAZZA_Intelligent_Biohazard_Training_Based_on_Real-Time_Task_Recognition_2016.pdf - Published Version Download (2MB) | Preview

Abstract

Virtual environments offer an ideal setting to develop intelligent training applications. Yet, their ability to support complex procedures depends on the appropriate integration of knowledge-based techniques and natural interaction. In this article, we describe the implementation of an intelligent rehearsal system for biohazard laboratory procedures, based on the real-time instantiation of task models from the trainee’s actions. A virtual biohazard laboratory has been recreated using the Unity3D engine, in which users interact with laboratory objects using keyboard/mouse input or hand gestures through a Kinect device. Realistic behavior for objects is supported by the implementation of a relevant subset of common sense and physics knowledge. User interaction with objects leads to the recognition of specific actions, which are used to progressively instantiate a task-based representation of biohazard procedures. The dynamics of this instantiation process supports trainee evaluation as well as real-time assistance. This system is designed primarily as a rehearsal system providing real-time advice and supporting user performance evaluation. We provide detailed examples illustrating error detection and recovery, and results from on-site testing with students from the Faculty of Medical Sciences at Kyushu University. In the study, we investigate the usability aspect by comparing interaction with mouse and Kinect devices and the effect of real-time task recognition on recovery time after user mistakes.

Item Type:	Article
Uncontrolled Keywords:	Bio-safety risk management, training application, virtual worlds
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science
Last Modified:	04 Mar 2022 13:07
URI:	http://gala.gre.ac.uk/id/eprint/19812

Actions (login required)

View Item

Downloads