Responsive wearables for rheumatoid arthritis

Abstract

The purpose of this thesis is to investigate and create more responsive and adaptive assistive technology for patients with rheumatoid arthritis (RA), using computational design methods to embed individualized data within the design and materiality. Rheumatoid arthritis is a chronic, autoimmune disease that attacks the joints and causes progressive deformity and bone erosion directed mostly at joint linings and cartilage. Living with RA means sudden flare-ups of pain and inflammation that can last anywhere from hours to months and dramatically impact the ability to accomplish ordinary tasks. While there is no cure, the disease can be slowed down through intensive drugs and or mitigated with assistive wearable devices such as braces, splints, and compressive gloves. These wearables are used to minimize swelling in affected joints, lessen ulnar deviating forces, and reduce pain. However, many people are unwilling to wear these devices because they can be quite obtrusive and hinder patients' lifestyles. Most wearables are only available in set sizes, and when sized incorrectly can aggravate pain and symptom flare-up or have no healing benefits. This thesis asks whether and how computational design methods can be applied to alleviating unique pain points faced daily by people with chronic health issues such as RA and other physical joint or musculature needs. Given that each person suffering from rheumatoid arthritis manifests the debilitating effects of the disease in different ways, this leads to the question of how more effective and personalized assistive devices can be designed using computational design methods that do not put the onus on the user to perform corrective action, but rather automatically offer responsive support as needed.