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EnglishSimulation 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 F.; L. MACCHIARULO; A. MONTUORI; E. PASERO; 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.
| Titolo: | Artificial Neural Networks for Motion Emulation in Virtual Environments |
| Autori: | |
| Data di pubblicazione: | 1998 |
| Abstract: | Simulation of natural human movement has proven to be a challenging problem, difficult to be solv...ed 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. |
| ISBN: | 3540493840 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 1413186.pdf | 2. Post-print / Author's Accepted Manuscript | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/1413186
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