  Human Activity Recognition (HAR) has become a spotlight in recent scientific
research because of its applications in various domains such as healthcare,
athletic competitions, smart cities, and smart home. While researchers focus on
the methodology of processing data, users wonder if the Artificial Intelligence
(AI) methods used for HAR can be trusted. Trust depends mainly on the
reliability or robustness of the system. To investigate the robustness of HAR
systems, we analyzed several suitable current public datasets and selected
WISDM for our investigation of Deep Learning approaches. While the published
specification of WISDM matched our fundamental requirements (e.g., large,
balanced, multi-hardware), several hidden issues were found in the course of
our analysis. These issues reduce the performance and the overall trust of the
classifier. By identifying the problems and repairing the dataset, the
performance of the classifier was increased. This paper presents the methods by
which other researchers may identify and correct similar problems in public
datasets. By fixing the issues dataset veracity is improved, which increases
the overall trust in the trained HAR system.
