Temporal Action Localization for Inertial-based Human Activity
  Recognition
Marius Bock Michael Moeller Kristof Van Laerhoven
A persistent trend in Deep Learning has been the applicability of machine
learning concepts to other areas than originally introduced for. As of today,
state-of-the-art activity recognition from wearable sensors relies on
classifiers being trained on fixed windows of data. Contrarily, video-based
Human Activity Recognition has followed a segment-based prediction approach,
localizing activity occurrences from start to end. This paper is the first to
systematically demonstrate the applicability of state-of-the-art TAL models for
wearable Human Activity Recongition (HAR) using raw inertial data as input. Our
results show that state-of-the-art TAL models are able to outperform popular
inertial models on 4 out of 6 wearable activity recognition benchmark datasets,
with improvements ranging as much as 25% in F1-score. Introducing the TAL
community's most popular metric to inertial-based HAR, namely mean Average
Precision, our analysis shows that TAL models are able to produce more coherent
segments along with an overall higher NULL-class accuracy across all datasets.
Being the first to provide such an analysis, the TAL community offers an
interesting new perspective to inertial-based HAR with yet to be explored
design choices and training concepts, which could be of significant value for
the inertial-based HAR community.