The goal of this work is the identification of humans based on motion data in the form of natural hand gestures. In this paper, the identification problem is formulated as classification with classes corresponding to persons' identities, based on recorded signals of performed gestures. The identification performance is examined with a database of twenty-two natural hand gestures recorded with two types of hardware and three state-of-art classifiers: Linear Discrimination Analysis (LDA), Support Vector machines (SVM) and k-Nearest Neighbour (k-NN). Results show that natural hand gestures allow for an effective human classification.
As many functionalities like cameras and MP3 players are converged to cell phones, more intuitive interaction methods are essential beyond tiny keypads. In this paper, we present gesture-based interactions and their two-stage recognition algorithm. Acceleration signals are generated from accelerometer. At the first stage, they are hierarchically modelled and matched as basic component and their relationships by Bayesian networks. At the second stage, they are further classified by SVMs for resolving confusing pairs. Our system showed enough recognition performance for commercialization; with 100 novice users, the average recognition rate was 96.9 on 11 gestures (digits 1-9, O, X). The algorithms have been adopted in the world-first gesture-recognizing Samsung cell phones since 2005.