C
Christian Cipriani
Researcher at Sant'Anna School of Advanced Studies
Publications - 158
Citations - 7861
Christian Cipriani is an academic researcher from Sant'Anna School of Advanced Studies. The author has contributed to research in topics: Sensory substitution & GRASP. The author has an hindex of 41, co-authored 147 publications receiving 6302 citations. Previous affiliations of Christian Cipriani include Imperial College London & IMT Institute for Advanced Studies Lucca.
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Journal ArticleDOI
Real-time myoelectric control of a multi-fingered hand prosthesis using principal components analysis
TL;DR: This work demonstrates that the proposed two-DoFs myoelectric controller based on PCA allows to drive in real-time a prosthetic hand emulator into different prehensile patterns with excellent performance, which opens up promising possibilities for the development of intuitive, effective myoelectedric hand controllers.
Book ChapterDOI
Design of Artificial Hands: A Review
TL;DR: This chapter is aimed at providing an overview of past and present artificial hands, developed in the frameworks of research projects in prosthetics and humanoid robotics.
Journal ArticleDOI
Stereovision and augmented reality for closed-loop control of grasping in hand prostheses
TL;DR: A controller based on stereovision to automatically select grasp type and size and augmented reality to provide artificial proprioceptive feedback is developed and is an effective interface applicable with small alterations for many advanced prosthetic and orthotic/therapeutic rehabilitation devices.
Journal ArticleDOI
Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces
Silvestro Micera,Silvestro Micera,Paolo Maria Rossini,Jacopo Rigosa,Luca Citi,Jacopo Carpaneto,Stanisa Raspopovic,Mario Tombini,Christian Cipriani,Giovanni Assenza,Maria Chiara Carrozza,Klaus P. Hoffmann,Ken Yoshida,Xavier Navarro,Paolo Dario +14 more
TL;DR: The possibility of decoding motor commands suitable to control a dexterous hand prosthesis was investigated for the first time by implementing a spike sorting and classification algorithm and showed that motor information could be extracted with classification accuracy around 85% and the user could improve his ability to govern motor commands over time.
Journal ArticleDOI
The SSSA-MyHand: A Dexterous Lightweight Myoelectric Hand Prosthesis
TL;DR: The SSSA-MyHand builds around a novel transmission mechanism that implements a semi-independent actuation of the abduction/adduction of the thumb and of the flexion/extension of the index, by means of a single actuator, and is as lightweight as conventional 1-Degrees of Freedom prostheses.