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Unsupervised Segmentation of Object Manipulation Operations from Multimodal Input

We propose a procedure for unsupervised identification of bimanual high-level object manipulation operations in multimodal data. The presented procedure applies a two-stage segmentation and a selection step to observation sequences. We employ an unsupervised Bayesian segmentation method to identify homogeneous segments which correspond to primitive object manipulation operations. The data is recorded using a contact microphone, a pair of Immersion CyberGloves and ten pressure sensors positioned on the fingertips. The assessment of the temporal correctness and structural accuracy of the segmentation procedure has showed satisfactory results. We have achieved an average error of 0.25 seconds in comparison to the actual segment borders. The examination of the structural accuracy for a given parameter combination has showed only insignificant deviation of the generated segmentation structure from the corresponding test data. Finally, we sketch an application of our method to unsupervised learning and representation of object manipulations.