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Bimanual gestures are of the utmost importance for the study of motorcoordination in humans and in everyday activities. A reliable detection ofbimanual gestures in unconstrained environments is fundamental for theirclinical study and to assess common activities of daily living. This paperinvestigates techniques for a reliable, unconstrained detection andclassification of bimanual gestures. It assumes the availability of inertialdata originating from the two hands/arms, builds upon a previously developedtechnique for gesture modelling based on Gaussian Mixture Modelling (GMM) andGaussian Mixture Regression (GMR), and compares different modelling andclassification techniques, which are based on a number of assumptions inspiredby literature about how bimanual gestures are represented and modelled in thebrain. Experiments show results related to 5 everyday bimanual activities,which have been selected on the basis of three main parameters: (not)constraining the two hands by a physical tool, (not) requiring a specificsequence of single-hand gestures, being recursive (or not). In the bestperforming combination of modeling approach and classification technique, fiveout of five activities are recognized up to an accuracy of 97%, a precision of82% and a level of recall of 100%.

Detection of bimanual gestures everywhere: why it matters, what we need and what is missing