  Injuries to the lower extremity joints are often debilitating, particularly
for professional athletes. Understanding the onset of stressful conditions on
these joints is therefore important in order to ensure prevention of injuries
as well as individualised training for enhanced athletic performance. We study
the biomechanical joint angles from the hip, knee and ankle for runners who are
experiencing fatigue. The data is cyclic in nature and densely collected by
body worn sensors, which makes it ideal to work with in the functional data
analysis (FDA) framework.
  We develop a new method for multiple change point detection for functional
data, which improves the state of the art with respect to at least two novel
aspects. First, the curves are compared with respect to their maximum absolute
deviation, which leads to a better interpretation of local changes in the
functional data compared to classical $L^2$-approaches. Secondly, as slight
aberrations are to be often expected in a human movement data, our method will
not detect arbitrarily small changes but hunts for relevant changes, where
maximum absolute deviation between the curves exceeds a specified threshold,
say $\Delta >0$. We recover multiple changes in a long functional time series
of biomechanical knee angle data, which are larger than the desired threshold
$\Delta$, allowing us to identify changes purely due to fatigue. In this work,
we analyse data from both controlled indoor as well as from an uncontrolled
outdoor (marathon) setting.
