Mackenzie Leake, Kathryn Jin, Abe Davis Stefanie Mueller.
InStitches: Augmenting Sewing Patterns with Personalized Material-Efficient Practice
In Proceedings of CHI ’23.



InStitches: Augmenting Sewing Patterns with Personalized Material-Efficient Practice

Figure 1. InStitches augments existing sewing patterns with targeted practice tasks that are efficient in terms of time and materials. Starting with an (a) input sewing pattern and instructions and a (b) user skill survey, it automatically (c) suggests practice steps and (d) provides a layout for creating accompanying practice pieces for tasks that the user is likely to find difficult. The user then (e) follows these interwoven practice and main pattern sewing steps to produce a (f) finished garment.

There is a rapidly growing group of people learning to sew online. Without hands-on instruction, these learners are often left to discover the challenges and pitfalls of sewing. Sewing is difficult to master, and those who explore it without a guide are left to discover many of its challenges through trial and error. This can be expensive, as each mistake comes at a material cost that can become a limiting factor.

In other domains in which the consequences of individual mistakes can be high—for example, competitive sports, or musical performance—a common mitigating strategy is engagement in deliberate practice, which focuses on performing tasks designed to improve the learner’s skills in lower-stakes settings. For example, in sports athletes will repeatedly perform drills for specific techniques; while in music, performers will practice scales or repeatedly play the most challenging segments from a longer piece of music. In sewing, however, deliberate practice is surprisingly uncommon.

The effectiveness of practice depends on being able to identify useful low-cost practice tasks that target the abilities and objectives of individual learners. In sewing, this can be a tall order; learners usually do not generally know which tasks will be difficult, and even when they do, it can be unclear what form low-cost practice should take. Practice, after all, still requires fabric. As a result, practice is uncommon in sewing and often amounts to multiple unsuccessful attempts at a project. Our work focuses on understanding the challenges that make practice in sewing so rare and building a tool to help mitigate those challenges in existing workflows.

We present InStitches, a software tool that augments existing sewing patterns with targeted practice tasks to guide users through the skills needed to complete their chosen project. InStitches analyzes the difficulty of sewing instructions relative to a user’s reported expertise in order to determine where practice will be helpful and then solves for a new pattern layout that incorporates additional practice steps while optimizing for efficient use of available materials. The design of InStitchesis driven by three key goals:

  1. Integration with existing workflows: Sewing is rich with tradition and established workflows. A system that is incompatible with these workflows is unlikely to have the same impact as one that complements them. With this in mind, InStitches is designed to work by augmenting existing sewing patterns and instructions.

  2. Personalization: For many learners, a key barrier to incorporating practice in sewing is not being able to identify when practice is necessary in the first place. Most new learners discover that a stitching task is difficult only when they fail to perform it. To be useful, our system needs to identify which tasks will be difficult before those tasks are performed. This requires incorporating knowledge of both the user’s skill level and the relative difficulty of different tasks.

  3. Efficient material use: A significant deterrent for practice in sewing is material use. Practice has a material cost, as one still needs fabric to practice on. Without careful planning, this cost can come to outweigh material saved by avoiding mistakes, so to benefit users, we need to ensure that the cost of practice remains low.

The first of these goals aims to minimize barriers to adoption, while the second two highlight aspects of our problem that are particularly well-suited for integration with a computational tool. In the case of personalization, computation offers a way to relate sewing patterns and user skill assessments to information harvested from texts on sewing. In the case of material efficiency, it lets us frame pattern layout as a 2D optimization problem, and by solving for layouts that minimize overall material cost, we can preferentially generate patterns that re-purpose previously wasted fabric to create dedicated practice pieces. Users are able to choose among practice tasks generated using three different strategies, each offering different material trade-offs. Our user evaluation indicates that InStitches is able to identify challenging sewing tasks and augment existing sewing patterns with practice tasks that users find helpful. This helps makers explore the craft of sewing in the context of a project of their choice.