Constable R, Thornhill R J, Carpenter D R
USAF AL/DOJE, Brooks AFB, TX 78235-5112.
Biomed Sci Instrum. 1994;30:69-74.
Surface electromyographic (SEMG) signals are often used to study motor control during human movement. Typically, the SEMG signal is used to determine when the muscle was on or off during the movement. However, determining the time-based frequency content of the SEMG signal in conjunction with the time-history of the movement may provide us with more insight into how the motion is organized. We collected SEMG from 4 muscles, (soleus, vasti, gluteus maximus, hamstrings) while subjects performed jumping and sit-to-stand tasks under altered g-environments. The experiment was performed on the Dynamic Environmental Simulator at Wright Patterson AFB, OH. The subjects performed the tasks at 1.0, 1.2, 1.4, 1.6, and 1.8 g. The SEMG signals were then analyzed using a continuous discrete wavelet transform based on a 4-coefficient Daubechie wavelet. Our initial results show that although the time-history of muscle activation patterns during movements do not always vary significantly at different g levels, the frequency content does change, with more high frequency activation at low g levels, and more low frequency activation at higher g levels. One possible explanation for the difference in frequency activation is fatigue of fast twitch fibers during the experiment. These differences were not apparent from the time-history activation patterns, and are difficult to interpret from standard FFT manipulations.
表面肌电图（SEMG）信号常用于研究人体运动过程中的运动控制。通常，SEMG信号用于确定运动过程中肌肉何时处于激活或放松状态。然而，结合运动的时间历程来确定SEMG信号基于时间的频率成分，可能会让我们更深入地了解运动是如何组织的。我们在受试者于改变的重力环境下进行跳跃和从坐姿到站姿的任务时，采集了四块肌肉（比目鱼肌、股四头肌、臀大肌、腘绳肌）的SEMG信号。实验在俄亥俄州赖特-帕特森空军基地的动态环境模拟器上进行。受试者在1.0、1.2、1.4、1.6和1.8g的重力水平下执行任务。然后使用基于4系数Daubechie小波的连续离散小波变换对SEMG信号进行分析。我们的初步结果表明，尽管运动过程中肌肉激活模式的时间历程在不同重力水平下并不总是有显著变化，但频率成分确实会改变，在低重力水平下高频激活更多，在高重力水平下低频激活更多。频率激活差异的一个可能解释是实验过程中快肌纤维的疲劳。这些差异从时间历程激活模式中并不明显，并且从标准的快速傅里叶变换操作中很难解释。
