PRO GAIT · Physiological and Rehabilitation Outcomes: Gains from Automated Interventions in stroke Therapy (PRO GAIT)

Physiological and Rehabilitation Outcomes: Gains from Automated Interventions in stroke Therapy (PRO GAIT)

THE PROBLEM Robotic walking devices are helpful after stroke to promote gait restoration. At present these devices provide assistance levels not well matched to the person's ability or intention to move. Responsive robots are required to advance stroke robotic rehabilitation. The PROGAIT study collects brain and muscle activity during robotic walking to better understand human machine interaction and to improve synchronisation between robot and person during gait. Allowing successful, repetitive task practice initiated and driven by the user to their maximum capability aligns with motor learning principles and neuroplastic drivers. SOCIETAL IMPACT PROGAIT makes an important societal contribution. Stroke is the leading cause of adult acquired disability; 3months after stroke 20% remain wheelchair dependent and 70% walk with reduced capacity resulting in stroke burden of 1.156 DALYs (Disability-Adjusted Life Years)/100.000, European disability-related care costs of €15.9 billion and productivity losses estimated at €4 billion. Stroke incidence is estimated to increase by 59% by 2030, a projection that could overwhelm services. Urgent strategies are required to meet escalating rehabilitation demands. Robotic assisted gait training (RAGT) can deliver intensity of practice with less human resources but requires developments, proposed in PROGAIT, to move from a passive therapy. While current deployment of RAGT improves the likelihood of walking independently after stroke, new knowledge in intent controlled RAGT could further restore efficient gait patterns, enabling more people to return home and contribute to society. OVERALL OBJECTIVES 1. establish the current state of the art in integration of neural biosignals with robotic gait devices towards a responsive robotic gait training after stroke 2. identify the acceptability and feasibility of robotic gait training in acute stroke and to explore if capturing electrical muscle activity levels during robotic gait training is useful and identifies optimal settings 3. collect electrical activity at brain level and muscle level during robotic walking after stroke and in healthy individuals to create a large data set 4. use the data set to identify if predictive modelling of gait is possible 5. explore the feasibility of using electrical biosignals to direct RAGT

Data: CORDIS, © European Union