We conducted a randomized controlled multicentre trial to investigate the feasibility of a telerehabilitation intervention for arm/hand function (the Home Care Activity Desk [HCAD] training) in a home setting. Usual care was compared to HCAD training. The hypothesis was that the clinical outcomes of the HCAD intervention would be at least the same as those measured after a period of usual care for patients with stroke, traumatic brain injury (TBI) and multiple sclerosis (MS) with respect to their arm/hand function. Eighty-one patients with affected arm/hand function resulting from either stroke, MS or TBI were recruited in Italy, Spain and Belgium; 11 were lost during follow-up (14%). The outcome measures were the Action Research Arm Test (ARAT) and the Nine Hole Peg Test (NHPT). There were no significant differences between the two groups on the outcome measures (ARAT and NHPT); in both groups, patients maintained or even improved their arm/hand function. The HCAD training was found to be as feasible as usual care in terms of clinical outcomes, and both therapists and patients were satisfied with the HCAD intervention. A telerehabilitation intervention using HCAD may increase the efficiency of care.

Here an inertial sensor-based monitoring system for measuring and analyzing upper limb movements is presented. The final goal is the integration of this motion-tracking device within a portable rehabilitation system for brain injury patients. A set of four inertial sensors mounted on a special garment worn by the patient provides the quaternions representing the patient upper limb’s orientation in space. A kinematic model is built to estimate 3D upper limb motion for accurate therapeutic evaluation. The human upper limb is represented as a kinematic chain of rigid bodies with three joints and six degrees of freedom. Validation of the system has been performed by co-registration of movements with a commercial optoelectronic tracking system. Successful results are shown that exhibit a high correlation among signals provided by both devices and obtained at the Institut Guttmann Neurorehabilitation Hospital.

No abstract

Brain-computer interfaces (BCIs) translate oscillatory electroencephalogram (EEG) patterns into action. Different mental activities modulate spontaneous EEG rhythms in various ways. Non-stationarity and inherent variability of EEG signals, however, make reliable recognition of modulated EEG patterns challenging. Able-bodied individuals who use a BCI for the first time achieve - on average - binary classification performance of about 75%. Performance in users with central nervous system (CNS) tissue damage is typically lower. User training generally enhances reliability of EEG pattern generation and thus also robustness of pattern recognition. In this study, we investigated the impact of mental tasks on binary classification performance in BCI users with central nervous system (CNS) tissue damage such as persons with stroke or spinal cord injury (SCI). Motor imagery (MI), that is the kinesthetic imagination of movement (e.g. squeezing a rubber ball with the right hand), is the "gold standard" and mainly used to modulate EEG patterns. Based on our recent results in able-bodied users, we hypothesized that pair-wise combination of "brain-teaser" (e.g. mental subtraction and mental word association) and "dynamic imagery" (e.g. hand and feet MI) tasks significantly increases classification performance of induced EEG patterns in the selected end-user group. Within-day (How stable is the classification within a day?) and between-day (How well does a model trained on day one perform on unseen data of day two?) analysis of variability of mental task pair classification in nine individuals confirmed the hypothesis. We found that the use of the classical MI task pair hand vs. feed leads to significantly lower classification accuracy - in average up to 15% less - in most users with stroke or SCI. User-specific selection of task pairs was again essential to enhance performance. We expect that the gained evidence will significantly contribute to make imagery-based BCI technology become accessible to a larger population of users including individuals with special needs due to CNS damage.