肌电图
信号(编程语言)
电阻抗肌描记术
肌肉疲劳
压力传感器
曲柄
生物医学工程
材料科学
压缩(物理)
分形维数
声学
计算机科学
分形
数学
医学
物理医学与康复
人工智能
工程类
心脏病学
物理
复合材料
血管舒张
数学分析
运动(物理)
机械工程
程序设计语言
作者
Aaron Belbasis,Franz Konstantin Fuss
标识
DOI:10.3389/fphys.2018.00408
摘要
= 0.84) compared to the EMG signal. This reflects that the pressure signal puts more emphasis on the fatigue as a function of time rather than on the origin of fatigue (e.g., peripheral or central fatigue). In light of the high-speed activity results, caution should be exerted when using data obtained from EMG for biomechanical models. In contrast to EMG data, activity data obtained from FMG are considered more appropriate and accurate as an input for biomechanical modeling as they truly reflect the mechanical muscle activity. In summary, the smart compression garment based on FMG is a valid alternative to EMG-garments and provides more accurate results at high-speed activity (avoiding the electro-mechanical delay), as well as clearly measures the progress of muscle fatigue over time.
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