Hand gestures are a form of non-verbal communication that is used in social interaction and it is therefore required for more natural human-robot interaction. Neuromorphic (brain-inspired) computing offers a low-power solution for Spiking neural networks (SNNs) that can be used for the classification and recognition of gestures. This article introduces the preliminary results of a novel methodology for training spiking convolutional neural networks for hand-gesture recognition so that a humanoid robot with integrated neuromorphic hardware will be able to personalise the interaction with a user according to the shown hand gesture. It also describes other approaches that could improve the overall performance of the model.

翻译：手部姿态是一种在社交互动中使用的非语言交流形式，因此更自然的人机交互需要对其加以识别。神经形态（类脑）计算为脉冲神经网络（SNNs）提供了低功耗解决方案，可用于姿态的分类与识别。本文介绍了一种训练脉冲卷积神经网络用于手部姿态识别的新方法初步成果，该方法使集成神经形态硬件的人形机器人能够根据用户展示的手部姿态实现个性化交互。同时，本文还描述了其他可提升模型整体性能的优化途径。