How might we use Industry 4.0 techniques and generative design to design a prosthetic that grows with the user and adapts to their lifestyle?
It became apparent through our research that a major problem trans-tibial amputees faced was socket-fit. The interface between the residual limb and prosthetic socket was not always flush, and this misalignment created added pressure on the residual limb. Excessive stress and uneven pressure distribution in prosthetic sockets causes discomfort, skin lesions, and muscle atrophy. This problem recurs as the residual limb heals and grows. While most amputees can communicate their socket comfort during the fitting process, creeping nerve damage in patients with peripheral neuropathy makes this difficult or impossible. As life-expectancy for patients with peripheral neuropathy increases the discomfort and damage due to poor socket-fit becomes a higher priority problem.
Even with a proper initial fit, short-term changes create new discomfort. The volume of the residual limb changes over time, and even throughout the day, making an accurate fit even more difficult. Follow-up appointments and re-fittings are necessary to ensure patient comfort.
It is estimated that as many as 41% of trans-tibial amputees suffer from poorly-fit sockets. This problem is more relevant today than ever as it is projected that the amputee population will more than double by the year 2050 to 3.6 million, primarily due to diabetes patients who are also at risk for peripheral neuropathy.
Our ADAPT Smart Sock is designed to fit a patient's residual limb and measure interface stresses between the residual limb and the prosthetic socket in real time. An array of force sensitive resistors are sewn into the spandex sock and their readings are taken on a microprocessor embedded in the prosthetic shin. This data is sent wirelessly to the prosthetist and can be used to inform patient health and prosthetic design. The design of both the socket and the prosthetic foot can be improved using the collected data.
Using Dynamo Studio we were able to use the Primer visual programming language to create logic-driven parametric conceptual designs.
Each parametric property of the template foot, including the Keel Height, Heel Distance, Spring Height, Midfoot Arc Height, Midfoot Arc Weight, and multipliers of each, is then seeded with random data to generate a wide sampling of first generation options. As can be seen below, many of these first generation feet are so primitive they did not even result in actual 3D geometry. These options are refined through user selection of preferential results, the inputs of which are then randomized and used to seed the subsequent generations following "Combined Favorites" rules of the Fractal Software:
Once an acceptable level of refinement has been reached (in our case usable geometry began to emerge around the fourth generation and was determined acceptable by the sixth generation) the resulting models can be exported to FEA simulation software to be analyzed against the data collected from the Smart Socket. For the purposes of this demonstration the downward force from the residual limb, as measure in the Smart Socket, was chosen as the simulation input.