  Human Activity Recognition (HAR), based on machine and deep learning
algorithms is considered one of the most promising technologies to monitor
professional and daily life activities for different categories of people
(e.g., athletes, elderly, kids, employers) in order to provide a variety of
services related, for example to well-being, empowering of technical
performances, prevention of risky situation, and educational purposes. However,
the analysis of the effectiveness and the efficiency of HAR methodologies
suffers from the lack of a standard workflow, which might represent the
baseline for the estimation of the quality of the developed pattern recognition
models. This makes the comparison among different approaches a challenging
task. In addition, researchers can make mistakes that, when not detected,
definitely affect the achieved results. To mitigate such issues, this paper
proposes an open-source automatic and highly configurable framework, named
B-HAR, for the definition, standardization, and development of a baseline
framework in order to evaluate and compare HAR methodologies. It implements the
most popular data processing methods for data preparation and the most commonly
used machine and deep learning pattern recognition models.
