Real-time classification of simultaneous hand and wrist motions using Artificial Neural Networks with variable threshold outputs
Paper in proceedings, 2013

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.

Author

Max Jair Ortiz Catalan

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Signals and Systems

Bo Håkansson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Biomedical Signals and Systems

Rickard Brånemark

University of Gothenburg

Proceedings of the XXXIV International Conference on Artificial Neural Networks (ICANN). Amsterdam, May 15-16, 2013

Subject Categories

Clinical Medicine

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Created

10/7/2017