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Social Signal Processing and Affective Computing with Machine Learning to Evaluate and Enhance Perceptual Amblyopia Treatment with Serious Games

About This PhD Project

Project Description


The proposed project aims to develop applied games driven techniques to evaluate and improve amblyopia through Social Signal Processing, Affective Computing and Machine Learning techniques. The project will utilise and be supported with various facilities within GCU, including the Games eMotion UX Lab and Vision facilities.

Social Signal Processing and Affective Computing are emerging research areas, which have similar overlapping goals. Social signal processing has the ambitious goal of bridging the social intelligence gap between computers and humans, while affective computing aims to develop human-computer interaction in which an electronic computing device has the ability to detect and appropriately respond to the user’s emotions and other stimuli.

Amblyopia, commonly known as lazy eye, is an eye condition, which is noticed by reduced vision. It is not correctable with glasses or contact lenses and is not due to any eye disease. The brain, for some reason, does not fully acknowledge the images seen by the amblyopic eye. Usually, amblyopia is a correctable problem if it is treated early in childhood. The classical treatment involves forcing the amblyopic (lazy) eye to work harder to see by blocking the vision in the good eye with a patch. Recently within GCU, a perceptual learning paradigm has been developed where children wear special gaming goggles while playing the game for an hour a day for up to 10 days. The goggles feed a clearer image to the lazy eye, forcing the brain to retrain with the lazy eye. Yet it has been found that while this process has been shown to successfully improve vision of participants, results have been unpredictable in that it isn’t clear which amblyopes will improve and which will not. A current hypothesis is that this improvement is directly related to the engagement of the participants during interaction of serious applied games.


The aim of this proposal is to develop novel methods in social signal processing and affective computing which can be utilised to assess the engagement of participants while playing an applied serious game, which utilises the above GCU perceptual learning paradigm. There will be three main components of the project.
1. The first is to investigate measures of emotional engagement with the serious games-based stimuli from biomedical responses (e.g. EEG using the Emotiv system, galvanic skin response, facial monitoring for eye tracking and heart rate from video, head movement).
2. The second will be to develop a tool to investigate correlations between user engagement and improvement in vision from serious game interactions.
3. The final stage is to provide a feedback loop, which aims to maximise the users engagement with process to try and maximise the improvement in vision through applied serious games.


The successful candidate is expected to have a solid mathematical background, strong programming skills (in C++/Python/Matlab), and keen interest in undertaking high-impact research work. Due to the nature of the project the candidate should also possess the ability to work cooperatively in a multi-disciplinary setting. Candidates from Games, Vision Science, Big Data, Electronic Engineering, Computing and Artificial Intelligence based backgrounds will be considered.

Research Strategy and Research Profile

Glasgow Caledonian University’s research is framed around the United Nations Sustainable Development Goals, We address the Goals via three societal challenge areas of Inclusive Societies, Healthy Lives and Sustainable Environments. For more. This project is part of the research activity of the Applied Games and Engaging Technologies Research Group.

Dr. Ryan Gibson [ECR], Department of Electrical and Electronic Engineering,


Example References
1. Mark D. Jenkins, Peter Barrie, Tom Buggy, and Gordon Morison, Selective sampling importance re-sampling particle filter tracking with multi-bag subspace restoration. IEEE Transactions on Cybernetics, (2016)
2. Ryan M. Gibson, Scott G. McMeekin, Ali Ahmadinia, Niall. C. Strang and Gordon Morison, A reconfigurable real-time morphological system for augmented vision. EURASIP J. Adv Signal Processing 134 (2013)
3. Pamela J. Knox, Anita J. Simmers, Lyle S. Gray and Marie Cleary An exploratory study: prolonged periods of binocular stimulation can provide an effective treatment for childhood amblyopia. Invest Ophthalmol Vis Sci. (2012)
4. Naeem Ramzan, Sebastian Palke, Thomas Cuntz Gibson, Ryan M. Gibson and Abbes Amira, Emotion recognition by physiological signals. Electronic Imaging (2016)

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