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：手势是一种非言语交流形式，用于社交互动，因此是实现更自然的人机交互所必需的。神经形态（受大脑启发）计算为可用于手势分类与识别的脉冲神经网络（SNNs）提供了低功耗解决方案。本文介绍了一种新颖方法的初步成果，该方法旨在训练脉冲卷积神经网络进行手部姿势识别，从而使配备集成神经形态硬件的人形机器人能够根据所展示的手部姿势个性化地与用户互动。文章还描述了其他可提升模型整体性能的方法。