Abstract
The paper presents an advanced method of recognition of patient’s intention to move of multijoint hand prosthesis during the grasping and manipulating objects in a dexterous manner. The proposed method is based on a two-level multiclassifier system (MCS) with heterogeneous and homogeneous base classifiers dedicated to EMG and MMG biosignals and with combining mechanism using a dynamic ensemble selection scheme and probabilistic competence function. The performances of two MCSs with the proposed competence function and combining procedure were experimetally compared against three benchmark MCSs using real data concerning the recognition of six types of grasping movements. The systems developed achieved the highest classification accuracies demonstrating the potential of multiple classifier systems with multimodal biosignals for the control of bioprosthetic hand.
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Kurzynski, M., Wolczowski, A. (2014). Hetero- and Homogeneous Multiclassifier Systems Based on Competence Measure Applied to the Recognition of Hand Grasping Movements. In: Piętka, E., Kawa, J., Wieclawek, W. (eds) Information Technologies in Biomedicine, Volume 4. Advances in Intelligent Systems and Computing, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-319-06596-0_15
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DOI: https://doi.org/10.1007/978-3-319-06596-0_15
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