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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
