This work documents the progress towards the implementation of an embedded solution for muscular forces assessment during cycling activity. The core of the study is the adaptation to a real-time paradigm an inverse biomechanical model. The model is well suited for real-time applications since all the optimization problems are solved through a direct neural estimator. The real-time version of the model was implemented on an embedded microcontroller platform to profile code performance and precision degradation, using different numerical techniques to balance speed and accuracy in a low computational resources environment.

A neural network embedded system for real-time estimation of muscle forces / Lozito G.M.; Schmid M.; Conforto S.; Riganti Fulginei F.; Bibbo D.. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - ELETTRONICO. - 51:(2015), pp. 60-69. [10.1016/j.procs.2015.05.196]

A neural network embedded system for real-time estimation of muscle forces

Lozito G. M.;
2015

Abstract

This work documents the progress towards the implementation of an embedded solution for muscular forces assessment during cycling activity. The core of the study is the adaptation to a real-time paradigm an inverse biomechanical model. The model is well suited for real-time applications since all the optimization problems are solved through a direct neural estimator. The real-time version of the model was implemented on an embedded microcontroller platform to profile code performance and precision degradation, using different numerical techniques to balance speed and accuracy in a low computational resources environment.
2015
51
60
69
Lozito G.M.; Schmid M.; Conforto S.; Riganti Fulginei F.; Bibbo D.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1299540
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