G
Giancarlo Ferrigno
Researcher at Polytechnic University of Milan
Publications - 350
Citations - 8848
Giancarlo Ferrigno is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Functional electrical stimulation & Kinematics. The author has an hindex of 44, co-authored 339 publications receiving 7296 citations. Previous affiliations of Giancarlo Ferrigno include University of Verona & Instituto Politécnico Nacional.
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Journal ArticleDOI
Elite: A Digital Dedicated Hardware System for Movement Analysis Via Real-Time TV Signal Processing
TL;DR: The system illustrated in this paper has been designed and developed particularly for automatic and reliable analysis of body movement in various conditions and environments and is based on real-time processing of the TV images to recognize multiple passive markers and compute their coordinates.
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Chest wall and lung volume estimation by optical reflectance motion analysis
S. J. Cala,C. M. Kenyon,Giancarlo Ferrigno,P. Carnevali,Andrea Aliverti,Antonio Pedotti,Peter T. Macklem,Dudley F. Rochester +7 more
TL;DR: In this paper, Chest wall and lung volume estimation by optical reflectance motion analysis is presented. But the method is not suitable for the analysis of lung tissue. And it cannot be used for lung cancer diagnosis.
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Human respiratory muscle actions and control during exercise
Andrea Aliverti,S. J. Cala,Roberto Duranti,Giancarlo Ferrigno,C. M. Kenyon,Antonio Pedotti,Giorgio Scano,Pawel Sliwinski,Peter T. Macklem,Sheng Yan +9 more
TL;DR: From QB to 0% Wmax there is a switch in respiratory muscle control, with immediate recruitment of rib cage and abdominal muscles, and a simple mechanism that increases drive equally to all three muscle groups allows the diaphragm to contract quasi-isotonically and act as a flow generator, while rib cage
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Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation
TL;DR: An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented that uses multiple color cameras and combines an accurate and anatomically consistent tracking algorithm with a method for automatically generating subject specific models.
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Improved recurrent neural network-based manipulator control with remote center of motion constraints: Experimental results.
TL;DR: An improved recurrent neural network (RNN) scheme is proposed to perform the trajectory control of redundant robot manipulators using remote center of motion (RCM) constraints to facilitate accurate task tracking based on the general quadratic performance index.