This study presents a multi-modal mechanism for recognizing human intentions while diving underwater, aiming to achieve natural human-robot interactions through an underwater superlimb for diving assistance. The underwater environment severely limits the divers' capabilities in intention expression, which becomes more challenging when they intend to operate tools while keeping control of body postures in 3D with the various diving suits and gears. The current literature is limited in underwater intention recognition, impeding the development of intelligent wearable systems for human-robot interactions underwater. Here, we present a novel solution to simultaneously detect head motion and throat vibrations under the water in a compact, wearable design. Experiment results show that using machine learning algorithms, we achieved high performance in integrating these two modalities to translate human intentions to robot control commands for an underwater superlimb system. This study's results paved the way for future development in underwater intention recognition and underwater human-robot interactions with supernumerary support.

翻译：本研究提出了一种水下人体意图识别的多模态机制，旨在通过水下超级机械臂实现自然的人机交互，为潜水提供辅助。水下环境严重限制了潜水员的意图表达能力，尤其是在需要控制身体姿态（三维空间内）、同时操作工具时，各种潜水服和装备进一步增加了难度。当前文献在水下意图识别方面存在局限，阻碍了用于水下人机交互的智能可穿戴系统的发展。本文提出了一种创新解决方案：通过紧凑可穿戴设计，在水下同步检测头部运动和喉部振动。实验结果表明，利用机器学习算法，我们在融合这两种模态以实现将人体意图转化为水下超级机械臂系统机器人控制指令方面取得了高性能。本研究结果为水下意图识别及基于超级数支持的水下人机交互的未来发展奠定了基础。