
Proceedings Paper
Analysis of different image-based biofeedback models for improving cycling performancesFormat | Member Price | Non-Member Price |
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Paper Abstract
Sport practice can take advantage from the quantitative assessment of task execution, which is strictly connected to the
implementation of optimized training procedures. To this aim, it is interesting to explore the effectiveness of biofeedback
training techniques. This implies a complete chain for information extraction containing instrumented devices,
processing algorithms and graphical user interfaces (GUIs) to extract valuable information (i.e. kinematics, dynamics,
and electrophysiology) to be presented in real-time to the athlete. In cycling, performance indexes displayed in a simple
and perceivable way can help the cyclist optimize the pedaling. To this purpose, in this study four different GUIs have
been designed and used in order to understand if and how a graphical biofeedback can influence the cycling
performance. In particular, information related to the mechanical efficiency of pedaling is represented in each of the
designed interfaces and then displayed to the user. This index is real-time calculated on the basis of the force signals
exerted on the pedals during cycling. Instrumented pedals for bikes, already designed and implemented in our laboratory,
have been used to measure those force components. A group of subjects underwent an experimental protocol and pedaled
with (the interfaces have been used in a randomized order) and without graphical biofeedback. Preliminary results show
how the effective perception of the biofeedback influences the motor performance.
Paper Details
Date Published: 1 February 2012
PDF: 10 pages
Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 829503 (1 February 2012); doi: 10.1117/12.910605
Published in SPIE Proceedings Vol. 8295:
Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
Karen O. Egiazarian; John Recker; Guijin Wang; Sos S. Agaian; Atanas P. Gotchev, Editor(s)
PDF: 10 pages
Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 829503 (1 February 2012); doi: 10.1117/12.910605
Show Author Affiliations
D. Bibbo, Univ. degli Studi di Roma Tre (Italy)
S. Conforto, Univ. degli Studi di Roma Tre (Italy)
I. Bernabucci, Univ. degli Studi di Roma Tre (Italy)
S. Conforto, Univ. degli Studi di Roma Tre (Italy)
I. Bernabucci, Univ. degli Studi di Roma Tre (Italy)
M. Carli, Univ. degli Studi di Roma Tre (Italy)
M. Schmid, Univ. degli Studi di Roma Tre (Italy)
T. D'Alessio, Univ. degli Studi di Roma Tre (Italy)
M. Schmid, Univ. degli Studi di Roma Tre (Italy)
T. D'Alessio, Univ. degli Studi di Roma Tre (Italy)
Published in SPIE Proceedings Vol. 8295:
Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II
Karen O. Egiazarian; John Recker; Guijin Wang; Sos S. Agaian; Atanas P. Gotchev, Editor(s)
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