Dynamic Hand Gesture Recognition Using LMC for Flower and Plant Interaction

Abstract

As the recent novel somatosensory devices become more pervasive, dynamic hand gesture recognition algorithm has attracted substantial research attention and has been widely used in the area of human–computer interaction (HCI). This paper aims to develop low-complexity and real-time solutions of dynamic hand gesture recognition using Leap Motion Controller (LMC) for flower and plant interactive applications. In this paper, we use two LMCs to obtain gesture data from different angles for fusion processing and then propose a novel feature vector, which adapts to representing dynamic hand gestures. After this, an improved Hidden Markov Model (HMM) algorithm was proposed to obtain the final recognition results, in which we apply the Particle Swarm Optimization (PSO) to avoid the complex computation of parameters in conventional HMM, thus improving the recognition performance. The experimental results on test datasets demonstrate that the proposed algorithm can achieve a higher average recognition rate of 96.5% for Leap-Gesture and 97.3% for Manipulation-Gesture. In addition, through the experiment of a flower and plant interaction, our dynamic gesture recognition solution can help users realize the interactive operation accurately and efficiently. In contrast to previous studies, our prototype system provides the users with a new dimension of experience and changes the research model of traditional forestry.