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：本研究提出了一种多模态机制，用于识别潜水员在水下作业时的人类意图，旨在通过水下超数肢体实现自然的人机交互以实现辅助潜水。水下环境严重限制了潜水员的意图表达能力，当他们在各种潜水服及装备辅助下需在三维空间中保持身体姿态控制同时操作工具时，这一挑战尤为显著。当前文献在水下意图识别领域的研究存在局限性，这阻碍了智能可穿戴系统在水下人机交互中的发展。本文提出了一种新颖解决方案，采用紧凑的可穿戴设计，同步检测水下头部运动与喉部振动。实验结果表明，通过机器学习算法，我们实现了将这两种模态进行高效整合，将人类意图转化为水下超数肢体系统的机器人控制指令。本研究结果为水下意图识别及超数支撑下水下人机交互的后续发展奠定了基础。