电阻抗肌描记术
可穿戴计算机
运动学
束
肌电图
计算机科学
生物医学工程
扭矩
电阻抗
功能性电刺激
肌肉收缩
矫形学
动力学(音乐)
电动机控制
康复机器人
接头(建筑物)
机器人学
肌肉结构
物理医学与康复
模拟
生物力学
腓肠肌
人工智能
特征(语言学)
人体肌肉
阻抗参数
解剖
膝关节
步态分析
材料科学
作者
Christopher J. Nichols,Harris Nicholas,H. Trask Crane,Autumn Routt,Milki Haile,Quentin Goossens,Minoru Shinohara,Gregory S. Sawicki,Kristen L. Jakubowski,Omer T. Inan
出处
期刊:Science robotics
[American Association for the Advancement of Science]
日期:2026-09-23
卷期号:11 (118): eaea4580-eaea4580
标识
DOI:10.1126/scirobotics.aea4580
摘要
Modern assistive robotic control demands parallel advancements in wearable, noninvasive sensing. Electrical impedance myography (EIM)-a noninvasive, wearable technology for dynamically measuring tissue electrical impedance properties-is a promising but poorly established solution for quantifying muscle force and fatigue, given that prior approaches have relied on black-box algorithms when attempting to relate EIM to muscle physiology or have extrapolated relationships between EIM and muscle force from singular, isolated movements. Here, we elucidate direct relationships between EIM and muscle kinematics and kinetics during functional movement using biomechanical principles and traditional measures of joint torque, muscle architecture, and activation. Dual-frequency EIM measurements of the medial gastrocnemius of 10 participants were collected and compared against dynamometer, B-mode ultrasound, and electromyography measurements. Participants performed multiple categories of dynamic movements spanning constrained isometric, self-selected isometric, concentric, and eccentric contractions at multiple force levels, joint angles, and angular velocities. EIM correlated to joint torque only for specific conditions, whereas EIM correlated with muscle length change dynamics across all movements. Mixed-effects modeling and sequential feature analysis showed fascicle length and muscle activation as primary drivers of EIM variance. Last, dual-frequency EIM combined with principal components analysis reliably captured changes in fascicle length and activation during walking. These findings validate EIM as a robust, real-time biomechanical tool for sensing muscle kinematics and neuromuscular activity during functional movement, expanding its use in assistive robotic control and injury prevention.
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