This proposed research aligns to the new Advanced Research and Engineering centre (ARC) within Northern Ireland. This Centre will drive future innovations in technology and enhance our capabilities in important research areas such as robotic process automation (RPA), workflow automation, visualisation, data analytics and artificial intelligence (AI). The Centre brings together expertise from PwC, University of Ulster and Queen’s University Belfast. PwC are a collaborating partner on this project and have committed to providing co-location, sharing of data and co-supervision, along with an additional cash contribution of approximately £6000 per year in addition to the normal stipend. Virtual and augmented reality (VR and AR) are rapidly maturing technologies that, as yet, have not yet delivered their full potential as a comprehensive, immersive and multi-modal interactive communications medium. Nor has VR/AR been extensively deployed as an interface to embed the human in a naturally symbiotic relationship with a vast body of digital data represented in multiple forms: text, images, video, audio. We know skill acquisition is essential for human development in order to attain high levels of competency in a professional capacity. As such, the demand for accessible and optimised learning and training systems is high. VR and AR demonstrate the potential to improve learning practices under certain conditions by introducing features such as haptics, enhanced visual information, and intelligent tutoring, that combine for a unique and dynamic pedagogical tool. With the advent of VR and AR, virtual systems could offer ubiquitous, effective, and affordable solutions to both learn and manage corporate systems and compliance processes. Virtual systems could one day be ubiquitous, and it will be important to observe the extent to which participants are learning for personalisation and optimisation within business process applications.

This project is required to build the necessary hardware and software to enable the collaborative and virtual presences required by systems such as those envisaged above, using a multi sensorial VR approach. The project also requires the development and examination of example scenarios. This requires the development of metrics against which to measure the effectiveness of such systems in the context of the example scenarios using techniques that provide quantitative assessment that are statistically rigorous.

There is a developing corpus of literature dedicated to EEG analysis techniques for human-machine systems, yet applications specifically for decision-making contexts are currently limited. Data is typically acquired directly from the human component of the system via the EEG headset, which is analysed. For example, the P300 signal is frequently used in medical science and neuroscience applications, but its potential for assisting in analysing learning and memory in a training, learning and decision-making context is promising. The relationship between the P300 and working memory suggests that the signal could be a valuable metric for detecting retention in virtual training systems, but there is little research in these applied environments evaluating the suitability for this purpose.

We propose using appropriate qualitative and quantitative analysis, including the analysis of appropriate user memory signals, to prove the VR, AR and AI can be combined to enhance the decision-making process within a business process.

Further Reading
Investigating the P300 Response as a Marker of Working Memory in Virtual Training Environments
Simpson, T. G. & Rafferty, K., 06 Apr 2021, (Early online date) In: IEEE Transactions on Human Machine Systems. 13 p.
Evaluating the Effect of Reinforcement Haptics on Motor Learning and Cognitive Workload in Training
Simpson, T. G. & Rafferty, K., 31 Aug 2020, International Conference on Augmented Reality, Virtual Reality and Computer Graphics. Springer, p. 203-211 (Lecture Notes in Computer Science)(Lecture Notes in Computer Science).

Funding Information

*Please note that funding is not available for international candidates*

To be eligible for consideration for a DfE Studentship (covering tuition fees and maintenance stipend of approx. £17,668 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications. The Studentship is open to UK and ROI nationals, and to EU nationals with settled status in the UK, subject to meeting the specific DfE nationality and residency criteria. Full eligibility information can be viewed via: https://www.economy-ni.gov.uk/publications/student-finance-postgraduate-studentships-terms-and-conditions

This is an industrially sponsored project. Approximately £6000 per year is payable to the sponsored student in addition to the stipend rate detailed above.

Academic Requirements:
A minimum 2.1 honours degree or equivalent in Computer Science, Electrical and Electronic Engineering, or Psychology or relevant degree with relevant technological experience.