###Diagnostics and rehabilitation after stroke in enriched XR+ML environments
Project ID: 2228bd1231 (You will need this ID for your application)
Research Theme: Healthcare Technologies
UCL Lead department: Queen Square Institute of Neurology
Lead Supervisor: Nick Ward
Project Summary:
The project draws together Bartlett expertise in spatial and embodied cognition, performance, and motion tracking in eXtended Reality (XR) and Machine Learning (ML) with expertise in neuroscience-based neurorehabilitation of post-stroke upper limb motor control to develop and design a technological solution to the problem of remote and networked stroke rehabilitation in spatially embedded virtual environments. While several rehabilitation VR games already exist, no platform with dynamically adaptable game mechanics exists that also uses individually available physical and spatial affordances. The project will develop a system that: automatically diagnoses individual patterns of impaired post-stroke motor control; allows practice of appropriate non-compensatory motor control in a motivating and adaptive environment; provides (remote) peer-to-peer interaction and support.
The student will learn how to bring together knowledge in neurorehabilitation with spatial cognition and performance to develop a sophisticated rehabilitation tool. This extends beyond ‘simple’ VR gaming environments by creating an adaptive technology to quantify impairments and tailor training to encourage therapeutically beneficial behaviours. The student will acquire knowledge of different patterns of post-stroke impairment (motor, sensory, cognitive) and how these influence patient rehabilitation. The student will construct an XR+ML system including movement tracking, recording, and measurement to create a spatially engaging therapeutic environment.