In this article, a head gesture recognition system is developed in order to identify emotional inputs and provide them to an adaptive music system (LitSens) in virtual reality applications, improving virtual presence in the process. Two iterations of this system, both founded on neural networks, are presented: the first one is based on a multi-layer perceptron, whereas the second one consists of a hybrid one-dimensional convolutional neural network. In both cases, the system is able to recognise fear by analysing head gestures. Whereas the first implementation is quicker when recognising this emotion, the second one is slower, but much more accurate, which makes it a better option overall for soundtrack adaptation. An experiment is then detailed, aimed towards validating the behaviour of a gestural recogniser when detecting fear in players. The results achieved through this validation are generally positive, but evince the need for an improvement in terms of system responsiveness.

在本文中，开发了一种手势识别系统，以识别情感输入并将其提供给虚拟现实应用程序中的自适应音乐系统（LitSens），从而改善过程中的虚拟状态。提出了该系统的两次迭代，均基于神经网络：第一个迭代基于多层感知器，而第二个迭代则由混合的一维卷积神经网络组成。在这两种情况下，系统都可以通过分析头部手势来识别恐惧。第一种方法在识别这种情绪时会更快，而第二种方法则较慢，但更为精确，这使其成为音轨适应的更好选择。然后详细介绍了一个实验，旨在验证玩家识别恐惧时手势识别器的行为。