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Human Activity Recognition (HAR), based on machine and deep learningalgorithms is considered one of the most promising technologies to monitorprofessional and daily life activities for different categories of people(e.g., athletes, elderly, kids, employers) in order to provide a variety ofservices related, for example to well-being, empowering of technicalperformances, prevention of risky situation, and educational purposes. However,the analysis of the effectiveness and the efficiency of HAR methodologiessuffers from the lack of a standard workflow, which might represent thebaseline for the estimation of the quality of the developed pattern recognitionmodels. This makes the comparison among different approaches a challengingtask. In addition, researchers can make mistakes that, when not detected,definitely affect the achieved results. To mitigate such issues, this paperproposes an open-source automatic and highly configurable framework, namedB-HAR, for the definition, standardization, and development of a baselineframework in order to evaluate and compare HAR methodologies. It implements themost popular data processing methods for data preparation and the most commonlyused machine and deep learning pattern recognition models.

B-HAR: an open-source baseline framework for in depth study of human activity recognition datasets and workflows