Abstract
Modern myoeletric prostheses necessitate more powerful control algorithms to derive hand movement information from sensor data. Common approaches utilize classifiers for recognizing motion or grip patterns from input data like myoeletric signals (MES). The selection of features for the classification process and the classifiers themselves impact the detection accuracy of the control schemes. In this contribution, we present a MATLABTM movement classification toolbox for sensor data recorded during hand movements. By covering different feature calculation- and classification-algorithms, the toolbox supports the modeling of select prostheses control schemes. In addition to MES, novel near-infrared (NIR) sensor input is equally supported by the toolbox. A modular development approach allows the integration of new features, classifiers as well as the extension to other types of sensor data.
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Attenberger, A., Buchenrieder, K. (2013). A MATLAB Toolbox for Upper Limb Movement Classification. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory - EUROCAST 2013. EUROCAST 2013. Lecture Notes in Computer Science, vol 8112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53862-9_25
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DOI: https://doi.org/10.1007/978-3-642-53862-9_25
Publisher Name: Springer, Berlin, Heidelberg
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