Ubiquitous self-tracking technologies' (STTs) adoption has taken a quantum leap in recent years, leading to a rapid increase in terms of volume, variety, and variability of the generated data from their embedded sensors. Consequently, integrating data from different self-tracking devices for further exploration and analysis has become time-consuming. In addition, it requires advanced technical skills, hindering their widespread adoption in interdisciplinary scientific and industrial research. This paper introduces an extensible, open-source framework and tool called WearMerge that automates the integration and transformation into a common standard of STTs' data across different brands and models. WearMerge aims to help and ease practitioners and researchers on STTs' data analysis.