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Real-time classification of simultaneous hand and wrist motions using Artificial Neural Networks with variable threshold outputs

Max Jair Ortiz-Catalan (Institutionen för signaler och system, Medicinska signaler och system) ; Bo Håkansson (Institutionen för signaler och system, Medicinska signaler och system) ; Rickard Brånemark
Proceedings of the XXXIV International Conference on Artificial Neural Networks (ICANN). Amsterdam, May 15-16, 2013 (2013)
[Konferensbidrag, refereegranskat]

Limb motions normally involve more than one degree of freedom combined in a coordinated manner. Although prosthetic hardware today could be combined for a highly motorized limb replacement, the control options available to amputees are so limited that this approach is rarely used. In this work, we introduce a classification strategy for the real-time simultaneous prediction of the individual movements present in natural motions. The real-time evaluation of this strategy based on a Multi-Layer Perceptron (MLP) with variable threshold outputs resulted in high motion completion rates. Moreover, the MLP alone showed higher offline accuracy than previously reported. This classifier was developed and evaluated in BioPatRec, an open source framework for advanced prosthetic control strategies based in pattern recognition algorithms. The source code and the data obtained in this study are freely available to be used for further algorithms development and benchmarking.

Denna post skapades 2013-04-25. Senast ändrad 2014-09-02.
CPL Pubid: 176132


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Institutioner (Chalmers)

Institutionen för signaler och system, Medicinska signaler och system (2005-2017)
Institutionen för kliniska vetenskaper, sektionen för anestesi, biomaterial och ortopedi (GU)


Klinisk medicin

Chalmers infrastruktur