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Valeria Villani

Researcher at University of Modena and Reggio Emilia

Publications -  69
Citations -  1234

Valeria Villani is an academic researcher from University of Modena and Reggio Emilia. The author has contributed to research in topics: Robot & Automation. The author has an hindex of 11, co-authored 62 publications receiving 708 citations. Previous affiliations of Valeria Villani include Università Campus Bio-Medico.

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Journal ArticleDOI

Smartwatch-Enhanced Interaction with an Advanced Troubleshooting System for Industrial Machines

TL;DR: This paper proposes the use of a smartwatch to interact with an advanced troubleshooting application to be used in industrial environment, which is a hypermedia information system aiming at assisting workers in performing preventive and corrective machine maintenance.
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An Adaptive Virtual Training System Based on Universal Design

TL;DR: This paper introduces an approach for the design of an adaptive virtual training system based on the idea of universal design that provides a flexible training system that can adapt to the needs of a broad group of users.
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MATE robots simplifying my work: benefits and socio-ethical implications.

TL;DR: In this paper, an integrated methodological design approach, called MATE, consisting in devising complex automatic or robotic solutions that measure current operator's status, adapting the interaction accordingly, and providing her/him with proper training to improve the interaction and learn lacking skills and expertise.
Proceedings ArticleDOI

Use of Virtual Reality for the Evaluation of Human-Robot Interaction Systems in Complex Scenarios

TL;DR: Use of virtual reality is considered as an alternative tool to assess HRI in those scenarios that are difficult to reproduce in reality and can be used to reliably validate human-robot interaction approaches in complex scenarios.
Journal ArticleDOI

Measurement and classification of human characteristics and capabilities during interaction tasks

TL;DR: Clusters of users are identified that, despite having different individual capabilities and features, have common needs and response to the interaction with complex production systems, and adaptation rules can be defined by considering such users’ clusters, rather than addressing specific individual user’s needs.