Design and Research on a Vision-based Lightweight Soft Ankle Exosuit

Abstract

There has been growing interest in the development of exosuit-assisted locomotion to effectively assist human motion. In this study, we present a novel soft ankle joint exosuit capable of providing dynamic support in varying terrains. The exosuit's underdriven structural design, combined with the use of visual sensors for real-time environmental detection, distinguishes it from existing solutions. The underdriven design enables the exosuit to achieve two degrees of freedom using a single actuator, thus improving its mobility and functionality. Additionally, we employ a semantic segmentation model based on deep learning to recognize the terrain of the environment and adapt the exosuit's assistance accordingly. The visual sensors provide real-time information to the system, allowing it to switch between assisted states and improve the exosuit's overall performance. We carried out experiments on a prototype to validate our proposed approach. With 83% accuracy of terrain recognition, the results demonstrate its feasibility and potential for future development.