A lightweight deep learning-free gesture recognition system by a local-connected reservoir computing model for service robots

Gesture recognition plays a crucial role in enabling intuitive and efficient human–robot interaction. Various deep learning-based methods have been recently proposed for implementing gesture recognition in robotic systems. However, the high computational cost of deep learning renders it less suitable for deployment in service robots with limited computational resources. This study aims to address this issue of computational cost by developing a lightweight, deep learning-free gesture recognition system based on reservoir computing (RC). Conventional RC models have the limitation that the spatial structure of the input data is often disrupted owing to random coupling during video processing. This study overcomes this limitation by proposing an RC model that incorporates spatial constraints into network coupling, thereby preserving the positional relationships of inputs during video processing. The experimental results reveal that the proposed method requires less processing time for both training and inference compared with an existing conventional RC-based method.