The story behind
Body Positive LAB
Inclusive technology for size recommendation based on anthropometric data.
The Origin
Body Positive LAB Body Positive LAB was founded in 2023 as a Service-Learning (SL) project developed with Fashion Design students. The initiative emerged from the need to reflect on body diversity, beauty standards, and the limitations of conventional sizing systems used by the fashion industry.
As the project's first action, an experimental prototype was developed using MIT App Inventor: MIT App Inventora mobile application designed to collect anthropometric measurements and explore new forms of interaction between users and digital recommendation systems. At the same time, body measurement activities were carried out in public spaces and educational settings, encouraging citizen participation and critical reflection on the relationship between body, identity, and fashion.
The experience revealed that body morphotypes are as diverse as fingerprints and that the same person may wear different sizes depending on the brand, country, or pattern-making system employed. These observations highlighted the need to develop more inclusive tools based on real-world data.
Following this exploratory phase, the project evolved towards the creation of its own anthropometric database and decision-support algorithms capable of comparing users' body measurements with standardized sizing systems. Today, Body Positive LAB brings together research in fashion design, digital anthropometry, Digital Humanities, and data science to develop more accurate, transparent, and inclusive size recommendation systems.
Body morphotypes are as unique as fingerprints.
Body Positive LAB
Body Diversity
Recognising body diversity as a starting point for inclusive fashion design. The project promotes a vision of fashion based on anthropometric data and real bodies rather than aesthetic stereotypes.
Digital Anthropometry
Development of digital tools for body measurement, analysis and data management. The research integrates anthropometric databases and size recommendation systems to support more accurate and inclusive decision-making
Size Recommendation Algorithms
Application of computational models and decision-support algorithms to improve size selection, reduce uncertainty in online purchasing and promote more inclusive user experiences.