The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the potential to digitize access to high-quality custom prosthetic and orthotic devices. This will increase convenience and reduce the cost of care for millions of people with limb loss or other disabilities. In 2018, the leading cause of amputations was complications from diabetes and peripheral vascular diseases, and 1.5 million new cases of diabetes were diagnosed among U.S. adults aged 18 years or older. Despite progress in other aspects of prosthetic limb design, inefficiencies in the design and manufacture of the custom prosthetic socket still exist. The proposed technology uses a computational approach to digitize the biomechanical design required to develop high-quality, custom prosthetic devices.
This Small Business Innovation Research (SBIR) Phase I project goal is to digitize and automate customization of wearable devices that conform to the portion of a person’s limb remaining after an amputation. The function of this device (i.e. prosthetic socket) is to re-distribute pressure to areas of the person’s soft tissues that tolerate those pressures. This project will introduce new guidelines and processes for prosthetic socket design. The objectives include: (1) codify, modify, and implement existing design guidelines within a biomechanics engine to manage customizations for the individual user; and (2) implement a machine learning framework using training datasets of paired limbs and prosthetic sockets. The techniques developed by this work can be extended to other biomechanical processes with load/pressure transfer considerations beyond prosthetics.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
