%0 Book Section %1 Stelzer2016 %A Stelzer, Patrick %A Kraus, Werner %A Pott, Andreas %B 47th International Symposium on Robotics, ISR 2016 %D 2016 %K Import180214 imported myown %P 265--270 %T Sensor glove for an intuitive human-machine interface for exoskeletons as manual load handling assistance %X 2016 VDE VERLAG GMBH Berlin Offenbach. Wearable assistive robots, also called exoskeletons, require an intuitive human-machine interface to reproduce human flexibility and to ensure user acceptance. In this paper, a concept for such an intuitive interface for assistance in manual handling of loads in industry is presented. The concept is based on the measurement of the interaction force between the human and the robot, on an impedance-based control approach and, in contrast to existing solutions, on an intelligent sensor glove. Suitable sensor types and sensor locations on the hand are selected. To show the feasibility of the concept a prototype of a sensor glove equipped with six piezoresistive force is developed and the force signals are recorded for two different grasp types. The resulting characteristic signal patterns could be recognized using a feedforward neural networks. Further, an outlook on future research is given. %@ 9783800742318

@incollection{Stelzer2016, abstract = {\copyright 2016 VDE VERLAG GMBH Berlin Offenbach. Wearable assistive robots, also called exoskeletons, require an intuitive human-machine interface to reproduce human flexibility and to ensure user acceptance. In this paper, a concept for such an intuitive interface for assistance in manual handling of loads in industry is presented. The concept is based on the measurement of the interaction force between the human and the robot, on an impedance-based control approach and, in contrast to existing solutions, on an intelligent sensor glove. Suitable sensor types and sensor locations on the hand are selected. To show the feasibility of the concept a prototype of a sensor glove equipped with six piezoresistive force is developed and the force signals are recorded for two different grasp types. The resulting characteristic signal patterns could be recognized using a feedforward neural networks. Further, an outlook on future research is given.}, added-at = {2018-02-14T08:37:06.000+0100}, author = {Stelzer, Patrick and Kraus, Werner and Pott, Andreas}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/253e4e43b29426443c600b6faca9a2d26/andreaspott}, booktitle = {47th International Symposium on Robotics, ISR 2016}, interhash = {3bfbff1849759488e060ce59b14037ad}, intrahash = {53e4e43b29426443c600b6faca9a2d26}, isbn = {9783800742318}, keywords = {Import180214 imported myown}, pages = {265--270}, timestamp = {2018-02-14T08:02:13.000+0100}, title = {Sensor glove for an intuitive human-machine interface for exoskeletons as manual load handling assistance}, type = {Publication}, year = 2016 }