Abstract
Exoskeleton gait rehabilitation is an emerging area of research, with potential applications in the elderly and in people with central nervous system lesions, e.g., stroke, traumatic brain/spinal cord injury. However, adaptability of such technologies to the user is still an unmet goal. Despite important technological advances, these robotic systems still lack the fine tuning necessary to adapt to the physiological modification of the user and are not yet capable of a proper human-machine interaction. Interfaces based on physiological signals, e.g., recorded by electroencephalography (EEG) and/or electromyography (EMG), could contribute to solving this technological challenge. This protocol aims to: (1) quantify neuro-muscular plasticity induced by a single training session with a robotic exoskeleton on post-stroke people and on a group of age and sex-matched controls; (2) test the feasibility of predicting lower limb motor trajectory from physiological signals for future use as control signal for the robot. An active exoskeleton that can be set in full mode (i.e., the robot fully replaces and drives the user motion), adaptive mode (i.e., assistance to the user can be tuned according to his/her needs), and free mode (i.e., the robot completely follows the user movements) will be used. Participants will undergo a preparation session, i.e., EMG sensors and EEG cap placement and inertial sensors attachment to measure, respectively, muscular and cortical activity, and motion. They will then be asked to walk in a 15 m corridor: (i) self-paced without the exoskeleton (pre-training session); (ii) wearing the exoskeleton and walking with the three modes of use; (iii) self-paced without the exoskeleton (post-training session). From this dataset, we will: (1) quantitatively estimate short-term neuroplasticity of brain connectivity in chronic stroke survivors after a single session of gait training; (2) compare muscle activation patterns during exoskeleton-gait between stroke survivors and age and sex-matched controls; and (3) perform a feasibility analysis on the use of physiological signals to decode gait intentions.
Highlights
Stroke has a high personal and societal burden
The resting state cortical activity and the gait characteristics pre- and post-training session will be recorded through EEG, EMG and inertial measurement units (IMU)—see Materials and Procedure
The key innovation of the proposed protocol is in the co-registration of data from three synchronised systems before, during and after robot-assisted gait-training to try to answer the following needs: 1
Summary
Stroke has a high personal and societal burden. It is the second most common cause of death (WHO fact sheet 2017) and a leading cause of adult physical disability [1], affecting17 million people worldwide each year. Stroke has a high personal and societal burden. It is the second most common cause of death (WHO fact sheet 2017) and a leading cause of adult physical disability [1], affecting. Demographic trends of an ageing population and escalation of risk factors will lead to an estimated 32% increase in DALYs (disability adjusted life years) lost from 2015 to 2035 (609,721 to 861,878) (WHO fact sheet 2017). Recovery after stroke is often incomplete with poor outcomes commonly reported. Improving recovery and long-term outcomes after stroke has become both a clinical and scientific challenge [2]. Despite conventional gait rehabilitation enhancing walking velocity, endurance [3], and balance, especially during the sub-acute phase [4], it can be physically onerous for therapists and challenging to facilitate an effective gait pattern for motor learning, in terms of cadence, inter-limb coordination, and muscle timing [5]
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