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Evaluation of simple microphone-based mechanomyography (MMG) probe sets for hand stiffness classification

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Języki publikacji
EN
Abstrakty
EN
We describe simple to build mechanomyography sensors, with one or two channels, based on electret microphones. We evaluate their application as a source of information about the operator’s hand stiffness, which can be used for changing a robot’s gripper stiffness during teleoperation. We explain a data acquisition procedure for further employment of a machine-learning. Finally, we present the results of three experiments and various machine learning algorithms. support vector classification, random forests, and neural-network architectures (fullyconnected articial neural networks, recurrent, convolutional) were compared in two experiments. In first and second, two probes were used with a single participant, with probes displaced during learning and testing to evaluate the influence of probe placement on classifcation. In the third experiment, a dataset was collected using two probes and seven participants. As a result of the singleprobe tests, we achieved a (binary) classification accuracy of 94 % during the multi-probe tests, large crossparticipant differences in classifcation accuracy were noted, even when normalizing per-participant.
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Twórcy
  • Lodz University of Technology, Stefanowskiego 18/22, 90-924 Lodz, www: www.robotyka.p.lodz.pl
  • Lodz University of Technology, Stefanowskiego 18/22, 90-924 Lodz, www: www.robotyka.p.lodz.pl
Bibliografia
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Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-447943ed-98df-40bb-b54e-796809b87a4f
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