|Reference Type||Conference Proceedings|
|Author(s)||Urban, S.; Bayer, J.; Osendorfer, C.; Wesling, G.; Edin, B.B..; Smagt, P. van der|
|Title||Computing grip force and torque from finger nail images using Gaussian processes|
|Journal/Conference/Book Title||Proc. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)|
|Abstract||We demonstrate a simple approach with which finger force can be measured from nail coloration. By automatically extracting features from nail images of a finger-mounted CCD camera, we can directly relate these images to the force measured by a force-torque sensor. The method automatically corrects orientation and illumination differences. Using Gaussian processes, we can relate preprocessed images of the finger nail to measured force and torque of the finger, allowing us to predict the finger force at a level of 95%-98% accuracy at force ranges up to 10N, and torques around 90% accuracy, based on training data gathered in 90s.
|Link to PDF||http://www.brml.org/uploads/tx_sibibtex/UrbBayOseWesEdiSma2013.pdf|