Connectivity Analysis of Functional Brain Networks in Using Multi-modal Human-Computer Interaction

To develop next generation multi-modal computer-aided design systems, it is important to evaluate the relationships between the user dependent factors and the combined performance of man and machine. The purpose of this research is to investigate if users’ cognitive activity would increase with the use of multi-modal input, speech and gestures by analysing EEG signals. Experiments are conducted, using traditional (keyboard and mouse) and multi-modal (speech and gesture) inputs. We used Normalized transfer entropy as a connectivity measure to find the information flow patterns. We constructed binary and weighted Functional Brain Networks to explore distinct and varied brain regions quantitatively. We found significant differences in cognitive activity between the traditional and multi-modal inputs. Our statistical analysis results state that the user’s cognitive activity increase when a multi-modal input is used. The findings have implications for the development of multi-modal interfaces for 3D modelling

Towards a Driving Training System to Support Cognitive Flexibility

Driving under unfamiliar conditions, such as unfamiliar traffic system and unfamiliar vehicle configuration during overseas holidays, might cause fatality, injury or property damage. In these cases, a driver needs to apply their prior knowledge to a new driving situation in order to drive safely. This ability is called cognitive flexibility. Prior research has found that left/mixed-handed people show superior cognitive flexibility in tasks required such ability than right-handed people. This paper aims to explore the relationships among cognitive flexibility, handedness and the types of errors drivers make, specifically at roundabouts and intersections in an unfamiliar driving condition. We conducted an experiment using a right-hand driving simulator and a left-hand simulated traffic scenario as a driving condition to collect the related data to driving at roundabout and intersection. All participants were not familiar with that condition. We found that left/mixed-handed drivers show a significantly superior cognitive flexibility at a turn-left roundabout and intersection. Also left/mixed handed drivers make a significantly fewer number of errors than right-handed drivers when entering the roundabout and approaching the intersection

Design and Evaluation of an Augmented Reality Game for Cybersecurity Awareness (CybAR)

The number of damaging cyberattacks is increasing exponentially due in part to lack of user awareness of risky online practices, such as visiting unsafe websites, ignoring warning messages, and communicating with unauthenticated entities. Although research has established the role that game-based learning can play in cognitive development and conceptual learning, relatively few serious mobile games have been developed to educate users about different forms of cyberattack and ways of avoiding them. This paper reports the development of an effective augmented reality (AR) game designed to increase cybersecurity awareness and knowledge in an active and entertaining way. The Cybersecurity Awareness using Augmented Reality (CybAR) game is an AR mobile application that teaches not only cybersecurity concepts, but also demonstrates the consequences of actual cybersecurity attacks through feedback. The design and evaluation of the application are described in detail. A survey was conducted to verify the effectiveness of the game received positive responses from 91 participants. The results indicate that CybAR is useful for players to develop an understanding of cybersecurity attacks and vulnerabilities

The Role of Computer Games in the Education of History

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The Role of Computer Games in the Education of History

13 page(s

Decoding 2D kinematics of human arm for body chine interfaces

Body-machine interface provides stroke and spinal cord injured patients a mean to participate in their activities of daily livings (ADLs). In this paper, electrophysiological signals from the human upper limb are used as a control interface between the user and a virtual robotic wheelchair. There is a general perception that these body signals contain an insufficient level of information for decoding or reconstructing kinematics of multi-joint limb activity. In this paper we present the results obtained in our virtual reality laboratory at Macquarie University, showing that non-invasive upper limb signals from high density wearable sensing shirt can be utilized to continuously decode the kinematics of 2D arm movements. Our results also show that body signals contain an information about the neural representation of movement. Moreover, they provide an alternative way for developing non-invasive body-machine interfaces, which have diverse clinical applications and access to these signals may provide understanding of functional brain states at various stages of development and aging.4 page(s

Using the SCORE software package to analyse novice computer graphics programming

This paper presents the SCORE (Student Coding Observation and Recording Engine) software package designed to capture and analyse student coding processes. The package consists of an Eclipse [1] plug-in to gather observational data while students code a programming task, and an analysis tool that allows researchers to visualise, categorise and annotate changes in code. Because the SCORE package supports code text level analysis it enables more in-depth understanding of student programming and problem solving approaches than meta-data or program output analysis tools. SCORE also provides features to assist the analysis of Computer Graphics programs. An example analysis of a student's Computer Graphics assignments demonstrates how SCORE was used to reveal the dominant role of general programming issues in the early assignment, whereas spatial programming issues persisted throughout both assignments.5 page(s