🤖 AI Summary
This study addresses the challenges of real-time monitoring of leg muscle states during underwater activity and the lack of automated emergency buoyancy assistance in the event of sudden cramps. The authors propose a wearable closed-loop system based on underwater mechanomyography (MMG), employing miniature microphones encapsulated in 5-mil polyethylene flexible waterproof membranes to capture muscle vibration signals. By integrating the MiniRocket classification algorithm with a pattern-based risk scoring model, the system achieves high-accuracy recognition of four swimming strokes and pre-cramp anomalies—attaining 91.91% accuracy at the window level and 97.56% at the file level—and triggers a CO₂ inflation mechanism to achieve automatic surfacing within five seconds. This work presents the first demonstration of stable MMG signal acquisition and real-time response in perturbed underwater environments, establishing a complete emergency buoyancy assistance pipeline spanning sensing, decision-making, and actuation.
📝 Abstract
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.