Abstract
Myoelectric signals (EMG) provide an intuitive and rapid interface for controlling technical devices, in particular bionic arm prostheses. However, inferring the intended movement from a surface EMG recording is a non-trivial pattern recognition task, especially if the data stems from low-cost sensors. At the same time, overly complex models are prohibited by strict speed, data parsimony and robustness requirements. As a compromise between high accuracy and strict requirements we propose to apply Echo State Networks (ESNs), which extend standard linear regression with (1) a memory and (2) nonlinearity. Results show that both features, memory and nonlinearity, independently as well as in conjunction, improve the prediction accuracy on simultaneous movements in two degrees of freedom (hand opening/closing and pronation/supination) recorded from four able-bodied participants using a low-cost 8-electrode-array. However, it was also shown that the model is still not sufficiently resistant to external disturbances such as electrode shift.
Funding by the DFG under grant number HA 2719/6-2, the CITEC center of excellence (EXC 277), and the Christian Doppler Research Foundation of the Austrian Federal Ministry of Science, Research and Economy is gratefully acknowledged.
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Prahm, C., Schulz, A., Paaßen, B., Aszmann, O., Hammer, B., Dorffner, G. (2017). Echo State Networks as Novel Approach for Low-Cost Myoelectric Control. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_40
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