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Real-time head gesture recognition on head-mounted displays using cascaded hidden Markov models

机译:使用级联的隐马尔可夫模型在头戴式显示器上进行实时头部手势识别

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Head gesture is a natural means of face-to-face communication between people but the recognition of head gestures in the context of virtual reality and use of head gesture as an interface for interacting with virtual avatars and virtual environments have been rarely investigated. In the current study, we present an approach for real-time head gesture recognition on head-mounted displays using Cascaded Hidden Markov Models. We conducted two experiments to evaluate our proposed approach. In experiment 1, we trained the Cascaded Hidden Markov Models and assessed the offline classification performance using collected head motion data. In experiment 2, we characterized the real-time performance of the approach by estimating the latency to recognize a head gesture with recorded real-time classification data. Our results show that the proposed approach is effective in recognizing head gestures. The method can be integrated into a virtual reality system as a head gesture interface for interacting with virtual worlds.
机译:头手势是人与人之间面对面交流的自然方式,但是很少有人研究在虚拟现实环境中识别头手势以及将头手势用作与虚拟化身和虚拟环境进行交互的界面。在当前的研究中,我们提出了一种使用级联隐马尔可夫模型在头戴式显示器上进行实时头部手势识别的方法。我们进行了两个实验,以评估我们提出的方法。在实验1中,我们训练了层叠隐马尔可夫模型,并使用收集的头部运动数据评估了离线分类性能。在实验2中,我们通过估计使用记录的实时分类数据识别头部手势的等待时间来表征该方法的实时性能。我们的结果表明,所提出的方法在识别头部手势方面是有效的。该方法可以被集成到虚拟现实系统中,作为用于与虚拟世界交互的头部手势接口。

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