Standardizing Your Training Process for Human Activity Recognition
  Models: A Comprehensive Review in the Tunable Factors
Yiran Huang Haibin Zhao Yexu Zhou Till Riedel Michael Beigl
In recent years, deep learning has emerged as a potent tool across a
multitude of domains, leading to a surge in research pertaining to its
application in the wearable human activity recognition (WHAR) domain. Despite
the rapid development, concerns have been raised about the lack of
standardization and consistency in the procedures used for experimental model
training, which may affect the reproducibility and reliability of research
results. In this paper, we provide an exhaustive review of contemporary deep
learning research in the field of WHAR and collate information pertaining to
the training procedure employed in various studies. Our findings suggest that a
major trend is the lack of detail provided by model training protocols.
Besides, to gain a clearer understanding of the impact of missing descriptions,
we utilize a control variables approach to assess the impact of key tunable
components (e.g., optimization techniques and early stopping criteria) on the
inter-subject generalization capabilities of HAR models. With insights from the
analyses, we define a novel integrated training procedure tailored to the WHAR
model. Empirical results derived using five well-known \ac{whar} benchmark
datasets and three classical HAR model architectures demonstrate the
effectiveness of our proposed methodology: in particular, there is a
significant improvement in macro F1 leave one subject out cross-validation
performance.