Automated detection of handovers using kinematic features. (June 2017) - Pan, Matthew KXJ; Skjervøy, Vidar; Chan, Wesley P; Inaba, Masayuki; Croft, Elizabeth A
This paper investigates the use of kinematic motions recognized by a support vector machine (SVM) for the automatic detection of object handovers from the perspective of an object receiver. The classifier uses the giver's kinematic behaviors (e.g. joint angles, distances of joints from each other and with respect to the receiver) to determine a giver's intent to hand over an object. We used a bagged random forest to determine how informative features were in predicting the occurrence of handovers, and to assist in selecting a core set of features to be used by the classifier. Altogether, 22 kinematic features were chosen for developing handover detection models and later testing of generalization performance. Test results indicated an overall maximum accuracy of 97.5% by the SVM in its capacity to distinguish between handover and non-handover motions. The classification ability of the SVM was found to be unaffected across four kernel functions (linear, quadratic, cubic and radial basis). These results demonstrate considerable potential for detection of handovers and other gestures for human–robot interaction using kinematic features.