Abstract
We present a novel dataglove mapping technique based on parameterisable models that handle both the cross coupled sensors of the fingers and thumb, and the under-specified abduction sensors for the fingers. Our focus is on realistically reproducing the posture of the hand as a whole, rather than on accurate fingertip positions. The method proposed in this paper is a vision-free, object free, data glove mapping and calibration method that has been successfully used in robot manipulation tasks.
The authors would like to acknowledge support from the German Collaborative Research Centre “SFB 673 - Alignment in Communication” granted by the DFG, and the German Center of Excellence 277 “Cognitive Interaction Technology” (CITEC).
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Steffen, J., Maycock, J., Ritter, H. (2011). Robust Dataglove Mapping for Recording Human Hand Postures. In: Jeschke, S., Liu, H., Schilberg, D. (eds) Intelligent Robotics and Applications. ICIRA 2011. Lecture Notes in Computer Science(), vol 7102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25489-5_4
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DOI: https://doi.org/10.1007/978-3-642-25489-5_4
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