Flagship 5: Augmentation of upper limb stroke rehabilitation
Will Browne (VUW), Winston Byblow (UoA), Simon Fraser (VUW), Angus McMorland (UoA), Edgar Rodriguez (VUW), Denise Taylor (AUT)
Stroke is a leading cause of adult disability worldwide. In New Zealand, approximately 7,800 people experience a stroke each year, and more than two-thirds of the country’s 64,000 stroke survivors require assistance with activities of everyday living. Impaired movement is common after stroke, and recovery of upper limb function is vital to regaining independence.
The goal of this Flagship is to develop assistive technologies for patients recovering from stroke. Our vision is to couple personalised data with computational models and appropriate behavioural change to enable those with disability to manage their own long-term health, and reduce the burden of health care.
Our assistive technologies cover a spectrum of complexity, from contextualised feedback using existing smart phones to novel EMG-driven controllers for robotic exoskeletons. There will be three main outcomes from this flagship:
1. A smart phone App to facilitate clinician-patient engagement in adaptive behavioural change.
The goal of this App is to provide contextualised feedback for effective self-management of patients with long-term neurological conditions. Using existing smart phone technology, this project will have an immediate impact to help patients manage their condition and is currently undergoing a clinical trial.
2. A software platform that integrates low-cost wearable sensors with computational models of the musculoskeletal system.
The ability to collect motion data from low-cost wearable sensors brings exciting new opportunities in terms of developing assistive devices to measure and monitor human movement. When combined with computational models of the musculoskeletal system, such systems will provide unprecedented ability to diagnose and treat movement disorders. We will create a software platform that integrates inertial, EMG and stretch sensors with musculoskeletal models to predict 3D motion and muscle and joint forces.
3. A real-time computational model of the upper limb that uses muscle synergies from EMG to predict forces and motion.
The use of robotic exoskeletons to augment stroke rehabilitation is an exciting area and one that is receiving much attention due to our ageing population. Existing controllers for such devices rely on predefined movement sequences or simplified force control feedback to take a patient through a range of motion. Our novel controller will use muscle excitations derived from EMG and an underlying model of muscle synergies to establish ‘intention of movement’ to control an exoskeleton device. We will also explore a combination of EMG with ECG to encode movement intention. Several applications of this novel controller and the underlying technology will be explored, including functional electrical stimulation and real-time haptic (tactile) feedback.