Novel Neural Interfaces For Upper-Limb Motor Rehabilitation After Stroke

Stroke is the third most common cause of death and the main cause of acquired adult disability in developed countries. The most common consequence of stroke is motor impairment, which becomes chronic in 56% of stroke survivors. However, reorganization of brain networks can occur in response to sensory input, expe- rience and learning. Although several post-stroke neurorehabilitation techniques have been investigated, there is no standardized therapy for severely impaired chronic stroke patients except for brain-machine interfaces (BMIs), which have shown positive results but still fail to elicit full motor function restoration. This work presents novel neural interfaces that aim to improve the existing rehabilita- tion therapies and to offer an alternative treatment to severely paralyzed stroke patients. First, we propose a novel myoelectric interface (MI) that is calibrated with electromyographic (EMG) data from the healthy limb, mirrored and used as a reference model for the paretic arm in order to reshape the pathological muscle synergy organization of stroke patients. A 4-session motor training with this mir- ror MI sufficed to induce motor learning in 10 healthy participants, suggesting that it might be a potential tool for the correction of maladaptive muscle activations and by extension, for the subsequent motor rehabilitation after stroke. Second, although significant positive results have been achieved with non-invasive BMIs based on electroencephalographic (EEG) activity, the functional motor recovery induced by such therapies still remains modest mainly due to poor decoding per- formance. Here, we explored the possibility of using novel algorithms to increase the performance of multi-class EEG-decoding of movements from the same limb, showing encouraging but still limited results. Finally, we propose integrating the novel mirror MI into a cortico-muscular hybrid BMI that combines brain and resid- ual muscle activity to increase decoding accuracy and hence, allow a more natural and dexterous control of the interface, facilitating neuroplasticity and motor re- covery. The system was validated in a healthy participant and a stroke patient, setting the premise for its application in a clinical setup

Subject-Independent Frameworks for Robotic Devices: Applying Robot Learning to EMG Signals

The capability of having human and robots cooperating together has increased the interest in the control of robotic devices by means of physiological human signals. In order to achieve this goal it is crucial to be able to catch the human intention of movement and to translate it in a coherent robot action. Up to now, the classical approach when considering physiological signals, and in particular EMG signals, is to focus on the specific subject performing the task since the great complexity of these signals. This thesis aims to expand the state of the art by proposing a general subject-independent framework, able to extract the common constraints of human movement by looking at several demonstration by many different subjects. The variability introduced in the system by multiple demonstrations from many different subjects allows the construction of a robust model of human movement, able to face small variations and signal deterioration. Furthermore, the obtained framework could be used by any subject with no need for long training sessions. The signals undergo to an accurate preprocessing phase, in order to remove noise and artefacts. Following this procedure, we are able to extract significant information to be used in online processes. The human movement can be estimated by using well-established statistical methods in Robot Programming by Demonstration applications, in particular the input can be modelled by using a Gaussian Mixture Model (GMM). The performed movement can be continuously estimated with a Gaussian Mixture Regression (GMR) technique, or it can be identified among a set of possible movements with a Gaussian Mixture Classification (GMC) approach. We improved the results by incorporating some previous information in the model, in order to enriching the knowledge of the system. In particular we considered the hierarchical information provided by a quantitative taxonomy of hand grasps. Thus, we developed the first quantitative taxonomy of hand grasps considering both muscular and kinematic information from 40 subjects. The results proved the feasibility of a subject-independent framework, even by considering physiological signals, like EMG, from a wide number of participants. The proposed solution has been used in two different kinds of applications: (I) for the control of prosthesis devices, and (II) in an Industry 4.0 facility, in order to allow human and robot to work alongside or to cooperate. Indeed, a crucial aspect for making human and robots working together is their mutual knowledge and anticipation of other’s task, and physiological signals are capable to provide a signal even before the movement is started. In this thesis we proposed also an application of Robot Programming by Demonstration in a real industrial facility, in order to optimize the production of electric motor coils. The task was part of the European Robotic Challenge (EuRoC), and the goal was divided in phases of increasing complexity. This solution exploits Machine Learning algorithms, like GMM, and the robustness was assured by considering demonstration of the task from many subjects. We have been able to apply an advanced research topic to a real factory, achieving promising results

Design and evaluation of a factorization-based grasp myoelectric control founded on synergies

In this article we present a factorization-based myoelectric proportional control that uses surface skin electromyographic (sEMG) measurements to estimate the hand closure level of a user for telemanipulation purposes. The sEMG-based proportional control design is presented and the results of an experimental session are reported. In particular, involving one healthy subject, four different factorization algorithms are tested (Factor Analysis, Fast Independent Component Analysis, Non-negative Matrix Factorization and Principal Component Analysis) and quantitative evaluated along four different daily session using four different error metrics (Root-Mean-Square Error, Normalized Root-Mean-Square Error, cross-correlation coefficient and Dynamic Time Warping measurement). The metrics are computed comparing the sEMG-based estimation of the hand closure level with a ground-truth signal obtained through a motion tracking system. The results report for better performances of the Non-negative Matrix Factorization algorithm, that can be used for controlling robotic hands in a real telemanipulation scenario. Therefore, the proposed myoelectric proportional control was finally tested in a simple validation grasping scenario using a real robotic hand, reporting for user's simplicity and intuitiveness in regulating the grasp closure in accordance with different objects

Sustainable Smart Cities and Smart Villages Research

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Union’s Cohesion Policy and Common Agricultural Policy Arguably, the concept of ‘the village’ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of ‘smart village’. It highlights in which ways ‘smart village’ is distinct from ‘smart city’. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.