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.
ABSTRACT
Severe acquired brain injury (ABI) is a major cause of long-term disability and is the main determinant of health and societal costs. Early identification of favourable long-term recovery would allow personalized rehabilitative programs and better health care resources allocation. In light of the higher survival rate from intensive care units (ICU) in recent years, there is a growing need for early prognostication markers of functional recovery; to date, these data have been mainly collected at rehabilitation unit admission and not during the acute phase. We present the protocol and methodology to develop prediction models in people with severe acquired brain injury (GCS at admission to ICU < 8) for the functional and cognitive outcome at 12 months from the event. Predictors will be collected during the acute stage. Participants will be recruited within the first 72 h from the event in the ICUs of two teaching hospitals (Padova and Treviso). Participants will be followed up at discharge from ICU, admission and discharge from Neurorehabilitation and after 12 months from the event. Clinical and functional scales, electroencephalography, evoked potentials, magnetic resonance imaging and serological markers will be entered into a digital registry. Survival will be estimated using the Cox proportional hazard model. A multivariate prediction model will be developed for each of the functional and cognitive outcomes at 12 months from the event.
