Real-Time and Simultaneous Control of Artificial Limbs Based on Pattern Recognition Algorithms
Journal article, 2014

The prediction of simultaneous limb motions is a highly desirable feature for the control of artificial limbs. In this work, we investigated different classification strategies for individual and simultaneous movements based on pattern recognition of myoelectric signals. Our results suggest that any classifier can be potentially employed in the prediction of simultaneous movements if arranged in a distributed topology. On the other hand, classifiers inherently capable of simultaneous predictions, such as the Multi-Layer Perceptron (MLP), were found to be more cost effective, as they can be successfully employed in their simplest form. In the prediction of individual movements, the One-Vs-One (OVO) topology was found to improve classification accuracy across different classifiers and it was therefore used to benchmark the benefits of simultaneous control. As opposed to previous work reporting only offline accuracy, the classification performance and the resulting controllability were evaluated in real-time using the Motion Test and Target Achievement Control (TAC) Test respectively. We propose a simultaneous classification strategy based on MLP that outperformed a top classifier for individual movements (LDA-OVO), thus improving the state-of-the-art classification approach. Furthermore, all the presented classification strategies and data collected in this study are freely available in BioPatRec, an open source platform for the development of advanced prosthetic control strategies.

Real-time systems

Pattern recognition




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

Sahlgrenska University Hospital

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

1558-0210 (ISSN)

Vol. 22 4 756-764

Subject Categories

Other Medical Engineering

Areas of Advance

Life Science Engineering (2010-2018)





More information

Latest update