  Muscle fatigue is usually defined as a decrease in the ability to produce
force. The surface electromyography (sEMG) signals have been widely used to
provide information about muscle activities including detecting muscle fatigue
by various data-driven techniques such as machine learning and statistical
approaches. However, it is well-known that sEMG signals are weak signals (low
amplitude of the signals) with a low signal-to-noise ratio, data-driven
techniques cannot work well when the quality of the data is poor. In
particular, the existing methods are unable to detect muscle fatigue coming
from static poses. This work exploits the concept of weak monotonicity, which
has been observed in the process of fatigue, to robustly detect muscle fatigue
in the presence of measurement noises and human variations. Such a population
trend methodology has shown its potential in muscle fatigue detection as
demonstrated by the experiment of a static pose.
