Simulation of natural human movement has proven to be a challenging problem, difficult to be solved by more or less traditional bio-inspired strategies. In opposition to several existing solutions, mainly based upon deterministic algorithms, a data-driven approach is presented herewith, which is able to grasp not only the natural essence of human movements, but also their intrinsic variability, the latter being a necessary feature for many ergonomic applications. For these purposes a recurrent Artificial Neural Network with some novel features (recurrent RPROP, state neurons, weighted cost function) has been adopted and combined with an original pre-processing step on experimental data, resulting in a new hybrid approach for data aggregation. Encouraging results on human hand reaching movements are also presented.

Artificial Neural Networks for Motion Emulation in Virtual Environments / Y., Bellan; M., Costa; G., Ferrigno; Lombardi, Franco; L., Macchiarulo; A., Montuori; Pasero, Eros Gian Alessandro; C., Rigotti. - 1537:(1998), pp. 83-99. (Intervento presentato al convegno Modelling and Motion Capture Techniques for Virtual Environments International Workshop, CAPTECH’98 tenutosi a Geneva (CHE) nel November 26–27, 1998) [10.1007/3-540-49384-0_7].

Artificial Neural Networks for Motion Emulation in Virtual Environments

LOMBARDI, FRANCO;PASERO, Eros Gian Alessandro;
1998

Abstract

Simulation of natural human movement has proven to be a challenging problem, difficult to be solved by more or less traditional bio-inspired strategies. In opposition to several existing solutions, mainly based upon deterministic algorithms, a data-driven approach is presented herewith, which is able to grasp not only the natural essence of human movements, but also their intrinsic variability, the latter being a necessary feature for many ergonomic applications. For these purposes a recurrent Artificial Neural Network with some novel features (recurrent RPROP, state neurons, weighted cost function) has been adopted and combined with an original pre-processing step on experimental data, resulting in a new hybrid approach for data aggregation. Encouraging results on human hand reaching movements are also presented.
1998
3540493840
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

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

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1413186
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo