Analysis of different image-based biofeedback models for improving cycling performances

D. Bibbo; S. Conforto; I. Bernabucci; M. Carli; M. Schmid; T. D'Alessio

doi:10.1117/12.910605

1 February 2012 Analysis of different image-based biofeedback models for improving cycling performances

D. Bibbo, S. Conforto, I. Bernabucci, M. Carli, M. Schmid, T. D'Alessio

Author Affiliations +

Proceedings Volume 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II; 829503 (2012) https://doi.org/10.1117/12.910605
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

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.

Citation Download Citation

D. Bibbo, S. Conforto, I. Bernabucci, M. Carli, M. Schmid, and T. D'Alessio "Analysis of different image-based biofeedback models for improving cycling performances", Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, 829503 (1 February 2012); https://doi.org/10.1117/12.910605

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