Article: Wearable robotic exoskeleton for overground gait training in sub-acute and chronic hemiparetic stroke patients: preliminary results - European Journal of Physical and Rehabilitation Medicine 2017 October;53(5):676-84 - Minerva Medica - Journals

Background: Overground Robot-Assisted Gait Training (o-RAGT) provides intensive gait rehabilitation. This study investigated the efficacy of o-RAGT in subacute stroke subjects, compared to conventional gait training. Methods: A multicenter randomized controlled trial was conducted on 75 subacute stroke subjects (38 in the Experimental Group (EG) and 37 in the Control Group (CG)). Both groups received 15 sessions of gait training (5 sessions/week for 60 min) and daily conventional rehabilitation. The subjects were assessed at the beginning (T1) and end (T2) of the training period with the primary outcome of a 6 Minutes Walking Test (6MWT), the Modified Ashworth Scale of the Affected lower Limb (MAS-AL), the Motricity Index of the Affected lower Limb (MI-AL), the Trunk Control Test (TCT), Functional Ambulation Classification (FAC), a 10 Meters Walking Test (10MWT), the modified Barthel Index (mBI), and the Walking Handicap Scale (WHS). Results: The 6MWT increased in both groups, which was confirmed by both frequentist and Bayesian analyses. Similar outcomes were registered in the MI-AL, 10MWT, mBI, and MAS-AL. The FAC and WHS showed a significant number of subjects improving in functional and community ambulation in both groups at T2. Conclusions: The clinical effects of o-RAGT were similar to conventional gait training in subacute stroke subjects. The results obtained in this study are encouraging and suggest future clinical trials on the topic.

Wearable powered robotic exoskeletons allow patients with complete spinal cord injury (SCI) to practice over-ground real-world gait scenarios. The global functional interaction subject-exoskeleton is a key factor to produce interlimb coordinated movements. Efficacy and efficiency of over-ground walking abilities using powered exoskeletons are related not only to the symbiotic sensory-motor interaction subject-exoskeleton but also to exoskeleton control. Assess if walking ability of motor complete SCI patients at thoracic or lower level, using a wearable powered exoskeleton (ReWalk), can be influenced by different exoskeleton software control. Observational study; an open, non-comparative, non-randomized study. A single neurological rehabilitation center for inpatients and outpatients. Fifteen SCI chronic patients (4 females and 11 males) were recruited and divided in two groups: group 1, trained with the first software generation of ReWalk, and group 2, trained with the second software generation, a software upgrade of the previous version. Subjects were trained during three 60-minute sessions a week, during at least eight weeks using ReWalk, a wearable lower limb powered exoskeleton that allows thoracic or lower level motor-complete individuals with SCI to walk, stand, sit and climb/descend stairs. Outcome measures, collected at the end of the training period wearing the exoskeleton, were: 6-min Walking Test, 10-m Walking Test, and the time necessary to pass from sitting to standing and start to walk (STS-time). For each group Pearson Coefficient was calculated to explore correlations between the subjects' characteristics and gait performance reached at the end of the training period. Group 1 showed correlation between performances and weight, height, neurological lesion level, while group 2 showed no correlation between performances weight and height, but correlation only with neurological lesion level. Group 2 covered more distance in 6 min (+124.52%) and required less time (-70.34%) to perform 10 mtWT and to STS-time (-38.25%) if compared to group 1. ReWalk allows chronic complete spinal cord injury patients to perform over-ground walking. Different exoskeleton software control of the smoothness of the gait pattern improves functional outcome, eliminating the relationship between anthropometric factors and gait performances. The smoothness of the kinematic control of the lower limbs of the exoskeleton is a key factor to facilitate human-robot interaction and to increase walking abilities of the subject. To underline how the kinematic control of the exoskeleton influences the walking abilities of the complex system subject-exoskeleton.

The recovery of motor functions after stroke is fostered by the functional integration of large-scale brain networks, including the motor network (MN) and high-order cognitive controls networks, such as the default mode (DMN) and executive control (ECN) networks. In this paper, electroencephalography signals are used to investigate interactions among these three resting state networks (RSNs) in subacute stroke patients after motor rehabilitation. A novel metric, the O-information rate (OIR), is used to quantify the balance between redundancy and synergy in the complex high-order interactions among RSNs, as well as its causal decomposition to identify the direction of information flow. The paper also employs conditional spectral Granger causality to assess pairwise directed functional connectivity between RSNs. After rehabilitation, a synergy increase among these RSNs is found, especially driven by MN. From the pairwise description, a reduced directed functional connectivity towards MN is enhanced after treatment. Besides, inter-network connectivity changes are associated with motor recovery, for which the mediation role of ECN seems to play a relevant role, both from pairwise and high-order interactions perspective.

Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week after stroke), and 35 stroke survivors during the early subacute phase (from 8 days to 32 days and after ∼2 months after stroke): We compared resting-state EEG fractal changes using fractal measures (i.e., Higuchi Index, Tortuosity) with 11 healthy controls. Both Higuchi index and Tortuosity values were significantly lower after a stroke throughout the acute and early subacute stage compared to healthy subjects, reflecting a brain activity which is significantly less complex. These indices may be promising metrics to track behavioral changes in the very early stage after stroke. Our findings might contribute to the neurorehabilitation quest in identifying reliable biomarkers for a better tailoring of rehabilitation pathways.