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