• robotic rehabilitation
• gait neurophysiology
• gait rehabilitation
• stroke

Stroke is one of the main causes of morbidity and mortality in adults in the developed world and the leading cause of disability in all industrialized countries. Hemiplegia is one of the most common impairments after stroke and contributes significantly to reduce gait capability. Although a considerable percentage of stroke patients achieve an independent gait, many do not reach a walking level that enable them to perform all their daily activities. Gait recovery is therefore a major objective in the rehabilitation program for stroke patients. Conventional gait training are not able to restore a normal gait pattern in the majority of stroke patients and robotic devices are increasingly accepted among many researchers and clinicians and are being used in rehabilitation of physical impairments in both the upper and lower limbs. These devices provide safe, intensive and task-oriented rehabilitation to people with mild to severe motor impairments after neurologic injury, but the mechanism by which they enhance recovery is still unknown.
On this purpose, the overall aim of this thesis was to describe the results of three series of experiments designed to investigate the neurophysiological mechanisms of human gait and the mechanisms underlying robotic-driven gait recovery in stroke patients so as to incorporate the findings of evidence-based practice into appropriate treatment plans for stroke motor rehabilitation.
In our first study we tested the efficacy of a 6-weeks gait retraining program with Lokomat (Hocoma AG, Switzerland) in 15 chronic stroke patients. We evaluated the effects using Fugl-Meyer Assessment score, Berg Balance Scale, 10 meters Walking Test, Timed Up and Go test and 6 Minute Walking Test, resulting in a significant improvement after the training in all scales (except 10 meters Walking Test). Strength and Motor Unit firing rate of Vastus Medialis during isometric knee extension were also recorded and analyzed: no increase of force was observed after the treatment, whereas a significant increase of firing rate of Vastus Medialis was recorded, suggesting an effect of training on motorneuronal firing rate that may contribute to the improvement of motor control.
In a second series of experiments, we aimed to design and implement a cooperative controller that combines Lokomat with Functional Electrical Stimulation (FES) of the quadriceps muscles. The goal of this new combined system was to enhance strength of the paretic leg (in particular to improve knee extension during the swing phase) and to boost motor learning through the reinstatement of an appropriate proprioceptive feedback directly triggered by residual EMG activity of the paretic leg. Three hemiparetic patients partecipated into 18 robotic gait training sessions; FES was triggered by surface EMG activity of the quadriceps of the paretic leg and synchronized to the Lokomat. Assessments were made before and after the treatment through clinical walking tests and 3D gait analysis; quadriceps strength was tested with an isokinetic dynamometer. At the end of the treatment, despite we didn’t observe an increase of muscle strength, patients improved their performance in the walking tests and gait analysis revealed an increase of walking speed and step length. Our newly developed system was demonstrated to be easily applied and efficient in improving gait in hemiparetic patients.
Mechanisms of motor learning similar to the ones involved in EMG-triggered FES application are supposed to be primed by decoding the patient intention directly from the brain activity. This approach, which is referred to as brain computer interfacing (BCI), requires more complex decoding methods than those based on muscular activities but provides a direct link with the neural circuitries activated during movement following the principles of a top-down approach. On this purpose, in our third series of experiments, we set up a MoBI lab (mobile brain/body imaging laboratory) to simultaneously record 64-channel EEG signals and lower-limb EMG signals from eleven able-bodied subjects who walked on a treadmill, while four footswitches enabled identification of the gait phase of each leg. Using a novel analysis technique based on a combination of Reliable Independent Component Analysis, source localization and effective connectivity, and by combining electroencephalographic (EEG) and electromyographic (EMG) recordings, we were able to examine for the first time cortical activation patterns and cortico-muscular connectivity including information flow direction. Results provided evidence of cortical activity associated with locomotion and also demonstrated significant causal unidirectional drive from contralateral motor cortex to muscles in the swing leg. These evidences overturn the traditional view of human stereotyped locomotion as an automated process, forcing reconsideration of gait as a complex process requiring supraspinal control even during stereotyped walking. The methodological framework developed and exploited in this last study could pave the way for new and more effective gait rehabilitation approaches in stroke and other neurological disorders. EEG signals could be used to develop more efficient ecological BCI systems for lower limbs (such as “brain-body” neuroprostheses) to restore locomotion through patients’ active participation.