SeamSmart

 

Automated Sewing Assessment

Download on the App Store
Code availabe at GitLab
Code availabe at GitLab

The automated sewing assessment research project aims to investigate and implement possibilities to support users in improving their sewing skills or filter for expertise-suitable tutorials by enabling individual sewing expertise algorithmically.

Publication Abstract

Makers must regularly assess their expertise when planning projects or selecting tutorials. However, personal bias makes this assessment prone to error, potentially leading to frustration, loss of materials, and discouragement. Additionally, hobbyists have limited feedback possibilities to refine their skills, unlike, for example, apprentice artisans who receive continuous instructor feedback. To address these issues, automated expertise assessment systems could help makers assess their skills and progress. However, such systems require assessment metrics, which have been studied little in the maker context so far. We derived such metrics for sewing from semi-structured interviews with ten sewing-related instructors about their evaluation process. Additionally, we showed them a sewn object and asked them to assess the creator’s expertise. From our findings, we derive criteria to use in future automated sewing expertise assessment systems. For one criterion, seam allowance, we present a functional demonstrator that automatically assesses related measurements.

Publication Videos

iOS Prototype

SeamSmart app workflow
For one criterion, seam allowance, we present a functional demonstrator that automatically assesses related measurements.

The iOS application code is available at:
https://git.rwth-aachen.de/i10/public/seamsmart

The following two images can be used as an example when testing the application

Contributors

Publications

    2024

  • Marcel Lahaye, Ricarda Rahm, Andreas Dymek, Adrian Wagner, Judith Ernstberger and Jan Borchers. How’s Your Sewing? Investigating Metrics to Automatically Assess Sewing Expertise.  In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, CHI '24, pages 7, Association for Computing Machinery, New York, NY, USA, May 2024.
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