Beyond Skin Deep: Generative Co-Design for Aesthetic Prosthetics

Title of the Paper

Beyond Skin Deep: Generative Co-Design for Aesthetic Prosthetics

Paper Information

• Thematic Areas: AI-driven generative design, assistive technology, personalized design, body aesthetics
• Keywords: generative design, body aesthetics, personalization, disabled dance artists, assistive technology, additive manufacturing, co-design

Research Background and Problem

• Identified Problems or Challenges:

  • Traditional prosthetics are primarily designed for functionality, focusing on comfort and physical support, while aesthetic needs are often overlooked.
  • The visual appearance of prosthetics typically aims to mimic natural human anatomy to conceal disabilities. However, there is a growing demand for fashionable and personalized designs.
  • Prosthetic users require richer ways to express their aesthetic preferences and personal identities, but custom prosthetic designs currently available are often expensive and difficult to produce.

• Significance of the Problem:

  • Aesthetics involve not only appearance but also functionality, experience, and identity. Empowering users to have greater control over prosthetic design can enhance their psychological well-being and social acceptance.
  • With the advancement of 3D printing technology, the cost of designing and manufacturing prosthetics has significantly decreased. New technologies can respond more quickly to user needs, including prosthetics designed for specific occasions or activities.

• Research Motivation and Related Work:

  • Aesthetic research has gradually expanded from a focus on visual aspects to include experience and bodily sensations.
  • In the design field, generative design algorithms have been proven to quickly produce design variations, offering possibilities for meeting personalized needs.
  • The intersection of disability and aesthetics is gaining increasing attention, with prosthetic aesthetics beginning to engage with identity dialogue, fashion statements, and sculptural exploration.

Solution

• Proposed Solution: The authors designed a co-design process that allows users to interact with algorithms. Through expressive dance, users generate personalized prosthetic "aesthetic seeds," which are applied to prosthetic shell designs and ultimately optimized for 3D printing.

• Innovations:

  • Introducing dance into the generative prosthetic design process, enabling disabled dance artists to participate in design through bodily expression.
  • Proposing the concept of "aesthetic seeds," where dance creates visualized design seeds, making the design process more personalized.
  • Combining generative design with additive manufacturing, not only imbuing prosthetics with aesthetic significance but also improving manufacturing efficiency and sustainability.

• Implementation Steps:

  1. Develop and test the generative design algorithm "Mogrow," which produces organic textures by dynamically adjusting parameters (e.g., injection rate, friction coefficient).
  2. Generate "aesthetic seeds" by interacting with Mogrow through dance movements.
  3. Apply a design mapping algorithm to transfer aesthetic seeds onto prosthetic shells and optimize designs to reduce material usage during 3D printing.
  4. Use various materials, such as transparent and wood-like options, to realize the generative designs, and refine them based on dancer feedback.
  5. Extract a general co-design framework through reflections on the design process and feedback from dancers.

Research Outcomes

• Specific Outcomes:

  • Developed a novel co-design method that enables users to interact with algorithms through their expressive skills to complete aesthetic prosthetic designs.
  • Achieved seamless integration of prosthetic design from visual aesthetics to functional optimization and sustainable manufacturing.
  • Created prosthetic samples with personal identity and aesthetic significance while maintaining high manufacturing efficiency.

• Advantages Over Existing Solutions:

  • Compared to traditional design models, this method gives users greater participation and autonomy in prosthetic design.
  • By leveraging generative design and 3D printing technologies, the cost and production time of custom prosthetics are reduced.
  • The technological process supports broader design goals, including non-functional, aesthetic, and political statements.

• Experimental or Evaluation Results:

  • Conducted two rounds of workshops with disabled dance artists to generate aesthetic seeds and design personalized prosthetic samples based on these seeds.
  • Feedback from dancers indicated a significant increase in their sense of participation in the design process, especially regarding the personal memories tied to the aesthetic seeds and the dance movements behind them.
  • The optimized prosthetic printing process saved approximately 30% in printing time and materials.

• Limitations and Future Directions:

  • Due to COVID-19 and the limited number of dancers involved, the study included only a small group of participants. Future research should expand the scale to evaluate the applicability to diverse user groups.
  • The method remains experimental; further exploration is needed to enable deeper user involvement throughout the process.
  • The current design process is linear; future research could explore more iterative co-design mechanisms.
  • Further development is needed for technologies that accommodate diverse body shapes, such as modular motion-capture suits.

This research provides a new perspective on personalized design and generative aesthetics while advancing the exploration of the intersection between disability identity and design.