title: Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.
abstract: 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).
authors: Institute of Electrodynamics, Microwave and Circuit Engineering, Vienna University of Technology, Gusshausstr. 25, 1040, Vienna, Austria. e0509285@student.tuwien.ac.at.|CD Laboratory for Extremity Reconstruction, Division of Plastic and Reconstructive Surgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria. e0509285@student.tuwien.ac.at.
authors: Cosima
authors: http://orcid.org/0000-0001-9379-5110
authors: C
authors: Prahm
authors: Department of Radiology and Nuclear Medicine, Medical University of Vienna, Lazarettgasse 14, 1090, Vienna, Austria.
authors: Korbinian
authors: K
authors: Eckstein
authors: Division for Biomedical Engineering, Department of Signals and Systems, Chalmers University of Technology, Campus Johanneberg, 41296, Gothenburg, Sweden.
authors: Max
authors: M
authors: Ortiz-Catalan
authors: Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
authors: Georg
authors: G
authors: Dorffner
authors: Institute of Electrodynamics, Microwave and Circuit Engineering, Vienna University of Technology, Gusshausstr. 25, 1040, Vienna, Austria.
authors: Eugenijus
authors: E
authors: Kaniusas
authors: CD Laboratory for Extremity Reconstruction, Division of Plastic and Reconstructive Surgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
authors: Oskar C
authors: OC
authors: Aszmann
mesh_terms: D000465:Algorithms; D001186:Artificial Limbs; D006801:Humans; D016014:Linear Models; D009068:Movement; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors
keywords: Machine learning; Neural computation; Pattern recognition; Prosthetics; Upper limb amputation