Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control
Journal article, 2016

Background: Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Methods: Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results: Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. Conclusions: It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).


Pattern recognition

Upper limb amputation

Neural computation

Machine learning


Cosima Prahm

Medizinische Universitaet Wien

Vienna University of Technology

Korbinian Eckstein

Medizinische Universitaet Wien

Max Jair Ortiz Catalan

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

Georg Dorffner

Medizinische Universitaet Wien

Eugenijus Kaniusas

Vienna University of Technology

Oskar C. Aszmann

Medizinische Universitaet Wien

BMC Research Notes

1756-0500 (ISSN)

Vol. 9 1

Subject Categories

Medical Engineering



More information

Latest update

9/6/2018 1