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A spiking network classifies human sEMG signals and triggers finger reflexes on a robotic hand

Tieck, J. C. V.; Weber, S.; Stewart, T. C.; Kaiser, J.; Roennau, A.; Dillmann, R. 1
1 Karlsruher Institut für Technologie (KIT)


The interaction between robots and humans is of great relevance for the field of neurorobotics as it can provide insights on how humans perform motor control and sensor processing and on how it can be applied to robotics. We propose a spiking neural network (SNN) to trigger finger motion reflexes on a robotic hand based on human surface Electromyography (sEMG) data. The first part of the network takes sEMG signals to measure muscle activity, then classify the data to detect which finger is being flexed in the human hand. The second part triggers single finger reflexes on the robot using the classification output. The finger reflexes are modeled with motion primitives activated with an oscillator and mapped to the robot kinematic. We evaluated the SNN by having users wear a non-invasive sEMG sensor, record a training dataset, and then flex different fingers, one at a time. The muscle activity was recorded using a Myo sensor with eight different channels. The sEMG signals were successfully encoded into spikes as input for the SNN. The classification could detect the active finger and trigger the motion generation of finger reflexes. The SNN was able to control a real Schunk SVH 5-finger robotic hand online. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000120545
Veröffentlicht am 18.08.2020
DOI: 10.1016/j.robot.2020.103566
Zitationen: 4
Zitationen: 5
Cover der Publikation
Zugehörige Institution(en) am KIT Universität Karlsruhe (TH) – Einrichtungen in Verbindung mit der Universität (Einrichtungen in Verbindung mit der Universität)
Forschungszentrum Informatik, Karlsruhe (FZI)
Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 09.2020
Sprache Englisch
Identifikator ISSN: 0921-8890, 1872-793X
KITopen-ID: 1000120545
Erschienen in Robotics and autonomous systems
Verlag Elsevier
Band 131
Seiten Art. Nr.: 103566
Schlagwörter Neurorobotics, Human–robot-interaction, Neural control system, Humanoid robot, Motion representation, sEMG classification, Spiking neural networks, Anthropomorphic robot hand
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