With the popularity of smart devices, it has become impossible for traditional human-computer interaction techniques to accommodate people's needs. This article proposes an iOS-based three dimensional (3D) gesture recognition system, gathering users' specific gestures from their handheld smart terminals to judge implications of these gestures, so to control other smart terminals with more natural human-computer interactions. In this article, gestures were recognized by reading data about corresponding 3D gesture data with motion sensors of smart terminals using optimized dynamic time warping (DTW) algorithm. As to this algorithm, curve paths were delimited via slope based on features of mobile devices and dynamic programming. Meanwhile, this algorithm reduced computational load for template matching and costs of gesture recognition by preliminarily storing upper and lower boundaries of delimited areas with linked lists or setting distortion thresholds. In this article, efficiency and precision of recognition schemes were tested and verified on cellphones. The results suggested that the improved algorithm was less time-consuming than classical algorithms, and required less time for computational load for template matching. Furthermore, it was demonstrated that the gesture recognition based on dynamic template matching algorithms, with higher recognition efficiency and precision, could bring better experiences of human-computer interactions.
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