A new approach for touch gesture recognition: Conversive Hidden non-Markovian Models

•We created two similar touch recognition systems (HMM and CHnMM based).•Two sets of very similar gestures are used for experiments.•CHnMM-based touch recognition reaches better recognition rates.•The CHnMM-system is computational competitive to HMM in practice.

With the current boom of touch devices the recognition of touch gestures is becoming an important field of research. Performing such gestures can be seen as a stochastic process, as there can be many little differences between different executions. Therefore stochastic models like Hidden Markov Models have already been applied to gesture recognition. Although the modelling possibilities of Hidden Markov Models are limited, they achieve an acceptable recognition quality. But they have never been tested with gestures that only differ in execution speed.We propose the use of Conversive Hidden non-Markovian Models for touch gesture recognition. This extension of Hidden Markov Models enhances the modelling possibilities and adds timing features. In this paper, two touch gesture recognition systems were developed and implemented based on these two model types. Experiments with a set of similar gestures show that the proposed model class is a good and competitive alternative to Hidden Markov Models.