Human-Machine System for the H2 Lower Limb Exoskeleton

Stroke is the leading cause of neurological disability in the United States and accounts for the poor physical health and the social dysfunction evident in survivors. Gait impairment is a large contributor to long-term disability and ambulatory function in daily living. Many patients, however, lose the ability to walk independently, and furthermore, a large proportion does not regain their normal walking speeds following a stroke. In this context, newer robotic-aided therapeutic tools such as "wearable" lower-limb robotic exoskeletons have been developed, which allow for the user to be augmented by mechanically actuated lower limb joints that can either completely or partially assist movements of the lower limb segments depending on the patient needs. The H2 exoskeleton (developed by Technaid S.L., Spain) is an example of one such system that has hip, knee and ankle joints actuated for both lower limbs. These devices are very new, and therefore, systematic investigations of therapeutic benefits of these devices are lacking in the field. Further, the nature of plasticity in the brain triggered by wearing and training such exoskeletons is unknown. In this exploratory research study, the investigators aim to compare robotic-assisted rehabilitation using the H2 exoskeleton with supervised motor practice particularly in terms of functional recovery. Additionally, this study will also examine brain plasticity associated with robotic-assisted training using non-invasive scalp electroencephalography (EEG) and changes in lower limb joint kinematics during robotic-assisted training. Taken together, the findings from this research will be used to understand human-robot interaction and to design smart powered orthotic devices that can be controlled directly by brain activity and assist those that have lost all or part of their walking abilities due to neurological disease or injury. Moreover, this study will systematically track neuroplasticity associated with functional recovery after stroke, which will help determine optimal windows for treatment that would maximize therapeutic benefit. Lastly, it will also help characterize markers of learning to use these new devices, which will be important in the clinical setting for modifying and adapting rehabilitation protocols to suit changing needs of the patient (user).