In this section
@article{Jin:2026:10.1109/LRA.2026.3666391,
author = {Jin, X and Gao, G and Wang, W and Vaidyanathan, R and Childs, P and Yu, Z},
doi = {10.1109/LRA.2026.3666391},
journal = {IEEE Robotics and Automation Letters},
pages = {4657--4664},
title = {Human-in-the-Loop Capacitive Microphone Sensors-Based Muscle Sensing System for Predictive and Adaptive Exoskeleton Assistance},
url = {http://dx.doi.org/10.1109/LRA.2026.3666391},
volume = {11},
year = {2026}
}
TY - JOUR
AB - Mobility impairments among older adults and individuals with neuromuscular weakness motivate the need for timely and adaptive exoskeleton assistance. This paper presents a human-in-the-loop muscle sensing and control system based on capacitive microphone sensors (CMS) that capture subtle mechanical muscle vibrations preceding observable motion. CMS signals were shown to occur 20-30 ms earlier than IMU-based kinematics, enabling anticipatory intent detection and feedforward assistive control. A five-sensor CMS array positioned over major thigh muscles is combined with a two-stage control strategy that integrates threshold-based pre-assist triggering and machine-learning-based torque refinement. Experiments across walking, stair ascent, sitting, and standing achieved over 90% classification accuracy under both non-fatigued and fatigued conditions with low latency. Robustness evaluations demonstrate stable CMS performance under realistic wearable perturbations, including perspiration and attachment variation. Extended experimental sessions (1-2 h) and preliminary feedback from five participants indicate comfortable wear and natural interaction. These results highlight the potential of CMS-based anticipatory sensing for practical wearable exoskeleton deployment in daily scenarios.
AU - Jin,X
AU - Gao,G
AU - Wang,W
AU - Vaidyanathan,R
AU - Childs,P
AU - Yu,Z
DO - 10.1109/LRA.2026.3666391
EP - 4664
PY - 2026///
SP - 4657
TI - Human-in-the-Loop Capacitive Microphone Sensors-Based Muscle Sensing System for Predictive and Adaptive Exoskeleton Assistance
T2 - IEEE Robotics and Automation Letters
UR - http://dx.doi.org/10.1109/LRA.2026.3666391
VL - 11
ER -