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.

Lozito, G.M., Schmid, M., Conforto, S., RIGANTI FULGINEI, F., Bibbo, D. (2015). A neural network embedded system for real-time estimation of muscle forces. In Procedia Computer Science (pp.60-69). Elsevier [10.1016/j.procs.2015.05.196].

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

Lozito, Gabriele Maria;Schmid, Maurizio;Conforto, Silvia;RIGANTI FULGINEI, Francesco;Bibbo, Daniele
2015-01-01

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
Lozito, G.M., Schmid, M., Conforto, S., RIGANTI FULGINEI, F., Bibbo, D. (2015). A neural network embedded system for real-time estimation of muscle forces. In Procedia Computer Science (pp.60-69). Elsevier [10.1016/j.procs.2015.05.196].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/313545
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