Extended Reality

We strive to research and develop innovative solutions to guide the future of the metaverse. By bringing together developers, researchers and designers, we can help build a brighter and more inclusive tomorrow.

Key areas of expertise:

The Extended Reality team with the MEPS division focus on the applications and platform design and development while engaging in active research and commercial activities across various sectors, as outlined in Figure 1.

• Digital Self 
  • Medical Diagnostics 
  • Holographics 
  • Social Cognisance 
  • Social Rehabilitation 

• VR Perception 
  • VR Vision 
  • Dynamic Adaptive Content 

• VR Tools
  • VR assisted Language Learning 
  • VR Mindfulness Monitoring 

• Tourism Sector : Use AR/VR for Heritage Interactive experiences 

• Big Pharma/Smart manufacturing: VR for education, training/Process Optimisation: Hyper Automation. 

Digital Patient Diagnosis, medical imaging and rehabilitation

The core concept we explore is the ability to model the patient themselves in a virtual space. This allows us to augment the virtual representation created by the scan with additional information or metrics and in the case of DigiHealthScan allow for the capturing, processing and display of health data in particular. This allows for the capture of the patient’s movement and patterns. By analysing this movement data through both classic methods or pose extraction and application of more complex methods such as deep learning algorithms, we can monitor and diagnose on the basis of postural gait and body part motion analysis.

Social Cognition: Capacity of Mixed Reality technology to enhance cognitive development as well as Social Cognitions development.

Impaired social communication represents a key obstacle facing people with varying conditions; this includes response to social stimuli as well as appropriate navigation of natural social settings. It is envisaged that this research shall also explore the user’s own awareness and recognition of their emotional state – through the use of ML algorithms and a body sensor array to indicate their own emotional state as well as that of others due to their behaviour in social situations, which can help to develop self-reflective skills. 

VR for Education

VR/AR technologies are becoming a more attractive option to support educational aspects. The group has linked with many clients in this space and continue to delve into new and innovative solutions and research in this area. Specific research activities here focus on aptitude tests, digital twin (for training and education) and examining the feasibility of virtual reality-assisted language learning in comparison to traditional methods. 

Mixed Reality Innovation lab

Funded by the Enterprise Ireland Capital Call and featuring state of the art immersive technology, the Mixed Reality Testbed lab offers ample resources encompassing:

• VR Head Mounted Displays (HMDs) – from the ubiquitous Oculus Quest 2 wireless headset to the higher quality Vive Pro Eye and PiMax 8K Vision Plus headsets.
• Mixed Reality HMD – Microsoft HoloLens 2.
• Live performance and acoustic capture – via Intel RealSense depth sensing (D435 model), tracking (T265 model) and LiDAR (L525 model) as well as GoPro Max 360s cameras, DJI Mavic Pro 2 Drone with a KAT VR omnidirectional treadmill and Teslasuit for motion capture and haptic feedback.
• Physiological sensing and eye tracking – solutions can be provided using the Neuroelectrics Enobio EEG headset and Pupil Labs Eye Tracking modules.

The Mixed Reality Testbed Lab offers an all-in-one space for exploring the use of interactive technologies and tools. This enables clients and collaborators to not only become familiar with the technology but also, with the aid of Walton Institute’s AR/VR team, to investigate how these technologies and concepts may be used to benefit their business and research use cases.

Additional Research Areas

• Image Detection and Responsivity Analysis of Embroidered Fabric Markers Using Augmented Reality Technology
• Facial emotion recognition analysis using deep learning through RGB-D imagery of VR participants through partially occluded facial types
• Body fat estimation accuracy based on volumetric 3D model generated from single view RGB imagery  and Improving body fat estimation accuracy with inferred bone density from volumetric 3D model from single view RGB imagery
• Investigation of the potential use of Virtual Reality for Agoraphobia Exposure therapy
• Comparison and Evaluation of Palliative Care Website Interactive Learning And Virtual Reality Learning
• Irish Sign Language in a Virtual Reality Environment