Disabled people and clinicians have a long history of inventing novel assistive technologies for themselves and others. This practice of creating assistive technology at the point-of-care is necessary to assure access to assistive technology. Disabled people, like all people, should be supported in augmenting their environment to their needs. Furthermore, their clinicians, as primary providers of assistive and medical devices,are best situated to deliver customized and personalized care.

It would at first appear that increased access to consumer-grade digital fabrication technologies like 3D printers would increase the scale and scope of these efforts. Unfortunately, the design tools needed to access digital fabrication are designed to primarily serve engineers, professional designers, or hobbyists. Such tools only leverage technical expertise, not the rich domain-expertise of disabled people and clinicians. A disabled person is uniquely qualified to express their needs and a clinician's medical expertise is often critical for designing assistive and medical devices that are verifiably safe and effective. Unfortunately, there is no way to express that expertise within current design tools.

I present two relevant bodies of research. The first is three qualitative studies of medical making, the practice of designing assistive and medical devices in a clinical setting or for use in a clinical setting. The second is the development of two computer aided design frameworks which support the (1) representation of domain specific expertise and (2) utilization of domain knowledge in generative design.

I focus on medical making, as opposed to general assistive making, because it is a critical access point where many people receive assistive devices. Medical makers form an new community of designers and digital fabricators whose ranks included clinicians, other healthcare providers, and disabled people. My qualitative research reveals that medical makers can recognize medical and assistive design requirements but struggle to use standard design tools to actualize a design that meets those requirements. That is, they can prescribe a solution, but they often struggle to make it. To meet the full potential of medical making, domain specific design tools would need to enable medical makers to specify their requirements, automatically generate designs to meet those requirements, and utilize design patterns from existing devices to inform this generative process.

I contribute two frameworks which enable medical makers to specify their intentions and generate designs that meet their specifications. My first framework, PARTs, scaffolds the 3D modeling process like object-oriented programming but in an entirely geometric, non-programmatic environment. This helps designers to add functionality and documentation to their design, amplifying their expertise as designs are shared and reused. My second framework, OPTIMUM structures the creation of domain-specific generative design tools, particularly those needed for medical making, around the mapping of objectives (i.e., designer goals) to modifiers (i.e., ways of reaching those goals). While OPTIMUM is derived from my design requirements of clinical CAD tools, it is also a generic and extensible framework which can be applied to a wide range of domains. I have developed five generative design systems using OPTIMUM in two medical domains, one assistive domain, and two fabrication domains. I present the final two systems, Maptimizer and KnitGIST in depth to demonstrate the capabilities of OPTIMUM as a toolkit for building domain specific generative design tools.

This body of work presents early findings in the burgeoning field of medical making. I recommend future work in the following areas. First, design tools should facilitate collaboration between patients and clinicians. Introducing new fabrication technologies into clinical practice puts clinicians and patients on equal footing as novices using these technologies. This may facilitate future collaborations between patients and clinicians that reduces medical paternalism and supports precision patient care. Second, as more 3D printable medical devices are created and shared in online communities, the potential risks of point-of-care facilities incorrectly selecting or producing a device could introduce significant medical risks. Medical making serves as a test bed for innovative medical device regulation through machine learning systems that can detect, and potentially correct, device risks or failures. Finally, medical making, like all fabrication applications, is limited by the materials makers can readily work with. In particular, textiles and soft materials are difficult to adapt to existing design tools. Extending the tool chains that support designing with a variety of material properties is critical to the future of medical making.

154 pages

Thesis Committee:
Scott E. Hudson (Co-Chair, Carnegie Mellon University)
Jennifer Mankoff (Co-Chair, University of Washington)
Patrick Carrington
Jodi Forlizzi
Bjorn Hartmann (University of California, Berkeley)

Jodi Forlizzi, Head, Human-Computer Interaction Institute
Martial Hebert, Dean, School of Computer Science

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