Too Good To Be True: performance overestimation in (re)current practices
  for Human Activity Recognition
Andrés Tello Victoria Degeler Alexander Lazovik
Today, there are standard and well established procedures within the Human
Activity Recognition (HAR) pipeline. However, some of these conventional
approaches lead to accuracy overestimation. In particular, sliding windows for
data segmentation followed by standard random k-fold cross validation, produce
biased results. An analysis of previous literature and present-day studies,
surprisingly, shows that these are common approaches in state-of-the-art
studies on HAR. It is important to raise awareness in the scientific community
about this problem, whose negative effects are being overlooked. Otherwise,
publications of biased results lead to papers that report lower accuracies,
with correct unbiased methods, harder to publish. Several experiments with
different types of datasets and different types of classification models allow
us to exhibit the problem and show it persists independently of the method or
dataset.