Monica Reggiani

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We aimed to evaluate the reliability of the modified Rankin Scale applied telephonically compared with face-to-face assessment in clinically stable hospitalized patients with acute stroke. One hundred and thirty-one patients were interviewed twice by 2 certified nurses (unstructured interview). Half of the patients were randomized to be interviewed by(More)
— The Programming by Demonstration paradigm promises to reduce the complexity incurred in programming robot tasks. Its aim is to let robot systems learn new behaviors from a human operator demonstration. In this paper, we argue that while providing demonstrations in the real environment enables teaching of general tasks, for tasks whose essential features(More)
Due to the high number of sensors managed and need to perform complex reasoning activities, real-time control systems of autonomous robots exhibit a high potential for overload, i.e., real-time tasks missing their deadlines. In these systems overload should be regarded as a likely occurrence and hence managed accordingly. In this paper we illustrate a novel(More)
This work examined if currently available electromyography (EMG) driven models, that are calibrated to satisfy joint moments about one single degree of freedom (DOF), could provide the same musculotendon unit (MTU) force solution, when driven by the same input data, but calibrated about a different DOF. We then developed a novel and comprehensive EMG-driven(More)
The technological developments in distributed systems have led to new telerobotic applications, such as virtual laboratories and remote maintenance of complex equipment. These applications must satisfy both the general requirements of distributed computing , e.g. location transparency and interoperability, and the domain-specific requirements of(More)
— The next generation of tools for rehabilitation robotics requires advanced human-robot interfaces able to activate the device as soon as patient's motion intention is raised. This paper investigated the suitability of Support Vector Machine (SVM) classifiers for identification of locomotion intentions from surface electromyography (sEMG) data. A(More)
Probabilistic path planning driven by a potential field is a well established technique and has been successfully exploited to solve complex problems arising in a variety of domains. However, planners implementing this approach are rather inefficient in dealing with certain types of local minima occurring in the potential field, especially those(More)