AI 中文总结
该研究开发了基于水下MMG的可穿戴紧急辅助系统，采用防水MMG传感器与MiniRocket分类器实现泳姿识别，可快速触发浮力部署，为水生环境下的肌肉状态监测与应急提供支撑。
AI 中文摘要
本文提出一种基于水下肌动电流图（MMG）的可穿戴紧急辅助系统，用于小腿肌肉状态监测与自动浮力部署。采用柔性5密耳聚乙烯（PE）膜对紧凑型麦克风式MMG传感器进行防水处理，使其在浸水、深度变化及水流扰动下仍能保留可识别的肌肉振动响应。在四种泳姿下，两个小腿传感器采集到与划水动作相关的MMG模式，MiniRocket分类器实现了91.91%的窗口级准确率和97.56%的文件级准确率。针对抽筋相关监测，采用基于模式的风险评分识别节律性运动中典型的抽筋前异常肌肉状态转变。受控水下测试验证了闭环感知-决策-执行链路，可在5秒内触发二氧化碳释放、气囊充气及漂浮。这些结果表明，水下MMG可作为水生环境中可穿戴机器人紧急辅助的传感基础。
英文摘要
This paper presents an underwater MMG-driven wearable emergency assistance system for lower-leg muscle-state monitoring and automatic buoyancy deployment. A compact microphone-based MMG sensor was waterproofed using a flexible 5 mil PE membrane, preserving identifiable muscle-vibration responses under immersion, depth variation, and stirring disturbances. Two lower-leg sensors captured stroke-dependent MMG patterns across four swimming styles, and a MiniRocket classifier achieved 91.91% window-level and 97.56% file-level accuracy. For cramp-related monitoring, a pattern-based risk score was used to identify representative pre-cramp abnormal muscle-state transitions during rhythmic motion. A controlled underwater test demonstrated the closed sensing--decision--actuation chain, triggering CO_2 release, airbag inflation, and flotation in less than 5~s. These results support underwater MMG as a sensing basis for wearable robotic emergency assistance in aquatic environments.
Comments8 pages, 14 figures
