A Systematic Review of Spiking Neural Networks for Human–Robot Interaction in Rehabilitative Wearable Robotics

Abstract

Recent advancements in spiking neural networks (SNNs) have highlighted their advantages, including energy efficiency, real-time processing, and compatibility with neuromorphic hardware. These features make SNNs particularly well-suited for human-robot interaction (HRI) in rehabilitative wearable robotics, where real-time adaptability and low power consumption are essential. However, there is still a lack of comprehensive reviews on SNNs’ application to HRI. This paper addresses this gap by providing a detailed overview of the latest advancements in SNNs from the perspective of embodied intelligence in rehabilitative wearable robots. We systematically examine recent progress in SNNs, including spiking neuron models, encoding methods, and learning mechanisms. These advancements are then analyzed with a focus on HRI, addressing specific challenges in rehabilitative wearable robots from three key perspectives: human motion decoding, robotic control, and neuromorphic implementation for embedded systems. By reviewing current research, this paper highlights the potential benefits and limitations of SNNs in achieving embodied intelligence and identifies crucial areas for further investigation, offering new insights and directions for their future applications in rehabilitative wearable robotics.