Title :
Simultaneous and proportional control of 2D wrist movements with myoelectric signals
Author :
Hahne, J.M. ; Rehbaum, H. ; Biessmann, F. ; Meinecke, F.C. ; Müller, K. -R ; Jiang, N. ; Farina, D. ; Parra, L.C.
Author_Institution :
Machine Learning Lab., Berlin Inst. of Technol., Berlin, Germany
Abstract :
Previous approaches for extracting real-time proportional control information simultaneously for multiple degree of Freedom(DoF) from the electromyogram (EMG) often used non-linear methods such as the multilayer perceptron (MLP). In this pilot study we show that robust control is also possible with conventional linear regression if EMG power measures are available for a large number of electrodes. In particular, we show that it is possible to linearize the problem with simple nonlinear transformations of band-pass power. Because of its simplicity the method scales well to high dimensions, is easily regularized when insufficient training data is available, and is particularly well suited for real-time control as well as on-line optimization.
Keywords :
biocontrol; electromyography; medical signal processing; multilayer perceptrons; prosthetics; regression analysis; robust control; 2D wrist movements; DoF; EMG power measures; MLP; band-pass power; electromyogram; insufficient training data; linear regression; multilayer perceptron; myoelectric signals; nonlinear transformations; online optimization; real-time control; real-time proportional control information multiple degree of Freedom; robust control; simultaneous control; Electromyography; Estimation; Joints; Linear regression; Proportional control; Trajectory; Wrist; Electromyography (EMG); linear regression; myoelectric control; simultaneous control; upper limb prosthesis;
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on
Conference_Location :
Santander
Print_ISBN :
978-1-4673-1024-6
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2012.6349712