Current and past research projects conducted by members of the IVES research group
COMPUTER-BASED REHABILITATION SYSTEM FOR STROKE SURVIVORS
Lead Investigator: Dr Mohd Fairuz Shiratuddin
This project focuses on designing, developing and evaluating a computer-based stroke rehabilitation system for stroke survivors. The project is still on-going, and we hope to do a pilot run soon.
Neuromend 1.0 (S2-2014)
The Neuromend 1.0 system was developed in S2-2014 as a final year project in ICT313 by our Games Technology, and Games Software Design & Production students.
Neuromend 1.0 comprises of hardware and software. The software was developed using the Unity 3D Game Engine for the 3D virtual environments, and game-based activities, and PhP on the backend data collection side. Neuromend 1.0 utilises different types of Natural User Interfaces (NUIs) devices which include Microsoft Kinect 1.0 for Windows, Razer Hydra and Leap Motion. It also utilises Oculus Rift DK1 head mounted display to display content in the 3D virtual environment.
Since we are targeting Neuromend 1.0 for stroke survivors who have gained good control of their upper-limb motor control, the game-based acitivities are designed to accomodate this.
VIDEO LINKS ARE COMING SOON. STAY TUNED !!!
Neuromend 2.0 (S1-2015)
The Neuromend 2.0 system was developed in S1-2015 as a final year project in ICT313 by our Games Technology, and Games Software Design & Production students.
Unlike Neuromend 2.0, Neuromend 2.0 tends to focus more on stroke survivors who have very little control of their upper-limb especially the elbow. Neuromend 2.0 is using Microsoft Kinect 2.0 as the NUI device. The software was developed using the Unity 3D Game Engine for the 3D virtual environments, and game-based activities, and PhP on the backend data collection side.
VIDEO LINKS ARE COMING SOON. STAY TUNED !!!
Neuromend 2.5 (ongoing in S2-2015)
Neuromend 2.5 is currently under development, and it will an extension of Neuromend 2.0 to include additional game-based tasks.
STAY TUNED !!!
MURDOCH DRIVING SIMULATOR
Lead Investigator: Mr Shri Rai
RADIOLOGY ASSISTANCE TRAINING SIMULATOR (RATS)
Lead Investigator: Dr Hong Xie
RATS is a simulator to train students on how to use radiology equipment such as an X-Ray machine. RATS encompases a real-time 3D virtual environment to simulate the actual environment itself. RATS provides a much safer option as it could avoid accidental exposure to radiation, it is low-cost and can be run on any average modern PCs. This project is still ongoing and will soon include the use of a Head Mounted Display to provide a much more immersive experience to the trainee.
RATS 1.5 (ongoing in S1-2015)
VIDEO LINKS ARE COMING SOON. STAY TUNED !!!
DIMENTIA PREVENTION USING VIRTUAL ENVIRONMENT
Lead Investigator: Associate Professor Dr Kevin Wong
The impact of Dementia on elderly is increasing in a fast pace in the recent years. Normally, exercise and diet are both factors that can help to prevent this problem. Cognitive activity is also another important factor to consider. While exercise and diet can be addressed by individual, cognitive activity for those individuals that involves some sort of social engagement can be difficult to find. Bingo, especially when played with peers, is commonly cited as an activity that can assist with training memory and information processing. For some seniors who are not mobile, it will be a problem for them to go somewhere and participate in a Bingo game. This project aim to look for an alternative using virtual environment and natural user interface. This project also examine ways to track and perform data analytics on the progress of the senior in such cognitive activity.
EMOTION RECOGNITION USING EEG
Lead Investigator: Associate Professor Dr Kevin Wong
Human Emotion is an important area of study in the Human Computer Interfaces (HCI) discipline. In this project, methods used to enhance the emotion recognition are investigated and developed. Efficient ways of handling emotion recognition or emotion classification is becoming more and more important with the advancement of Brain Computer Interface (BCI) in many applications. This project is focusing more on real time emotion recognition using EEG.
DATA MINING WITH IMBALANCED DATA
Lead Investigator: Associate Professor Dr Kevin Wong
The class imbalance problem is one of the important problem exist in many data. An imbalanced data set could be one of the obstacles for several machine learning algorithms. Many existing algorithms cannot be used directly to handle the imbalanced data problem especially in multi-class classification. This project investigates ways and strategies to handle the imbalanced problem in many current data analytic problems.
FUZZY RULE INTERPOLATION
Lead Investigator: Associate Professor Dr Kevin Wong
Fuzzy logic can be found in many applications ranging from domestic products like washing machine to many commercial control systems. However, the nature of data for current applications may pose many challenges for current fuzzy system. Fuzzy rule interpolation (FRI) techniques were introduced to generate inference for sparse fuzzy rule base to perform inference with a reduced set of fuzzy rules. Basically, FRI techniques perform interpolative approximate reasoning by taking into consideration the existing fuzzy rules for cases where there is no fuzzy rules to fire. See http://fri.gamf.hu/
FUZZY SIGNATURE AND COGNITIVE MODELLING
Lead Investigator: Associate Professor Dr Kevin Wong
As data is getting more complex and complicated, it is increasingly difficult to construct an effective complex decision model. Fuzzy signatures are introduced to handle complex structured data and problems with interdependent features. A fuzzy signature can also be used in cases where data is missing. Fuzzy signatures can address some issues of granulation and organisation well. In order to better model the human cognitive system, we have divided our cognitive modelling into two main categories. The first category consists of meta-levels of visual representation to model decision and cognitive behaviour. In this category the model consists of nodes and pointers to show the concepts and relations. Each node exhibits the behaviour of a human cognitive system. Each node consists of three states, the sensory input state, current state, and action state. In the second category, nodes basically consist of the fuzzy signatures. These signatures contain the knowledge necessary for the node to take any action.
EMERGENCY EVACUATION SIMULATOR
Lead Investigator: Mr Shri Rai
FORMATION OF THE INTERNATIONAL WOMEN’S EDUCATIONAL LEADERSHIP FORUM, AN ORGANISATION WHICH AIMS TO PROVIDE TARGETED MENTORING FOR YOUNG WOMEN AND THOUGHT LEADERSHIP FOR GLOBAL EDUCATION
Lead Investigator: Professor Sara de Freitas
SCIENCE OF LEARNING PROJECT TO INVESTIGATE EFFICACY OF LEARNING IN DIFFERENT MODES THROUGH NEUROPSYCHOLOGY TECHNIQUES USING EEGS
Lead Investigator: Professor Sara de Freitas
A CROSS-DISCIPLINARY AND EVIDENCE-BASED STUDY OF HUMAN LEARNING
Lead Investigator: Dr Victor Alvarez
Current topics include:
- Exploring the potential of Augmented Reality (AR) and Wearable Technologies (WT) in capturing live experience and creating an immersive learning experience.
- Establishing a strategic approach to use and implementation of learning analytics (LA) in higher education.
- Science of Learning project to investigate efficacy of learning in different modes through neuropsychology techniques using EEGs.