Juvenile idiopathic arthritis (JIA) is the most common form of childhood arthritis and affects more than 50,000 children in the US. Symptoms and progression of JIA vary greatly between individuals, and there are many possible treatment options depending on the extent of disease. However, diagnosing JIA on a patient-by-patient basis is difficult because reliable tools for assessing the condition do not yet exist, and treatment is often a trial-and-error process. This project will focus on developing wearable joint health-sensing systems that continuously provide data on joint condition, inside and out of the clinic. The PI will focus on advancing the ability to measure and analyze sounds produced by joints during movement. The study will test the hypothesis that joint sounds decrease as joint health improves with therapy. Sounds collected with the wearable devices will be analyzed and compared with patients' joint health to understand possible relationships that can be used to improve diagnostic capabilities. Research and education are integrated through the incorporation of undergraduate research teams in the sensing brace design process and incorporating de-identified dataset analysis in courses at Georgia Institute of Technology. Additionally, the PI will host K-12 teachers in his lab to develop science and engineering modules to take back their classrooms.
Dr. Inan's long-term career goal is to fundamentally advance knowledge in the field of wearable joint health sensing and, thereby, enable personalized therapies and devices for patients with joint disorders, such as arthritis. Toward this goal, Dr. Inan's research objectives are to: (1) design and implement a sensing brace capable of accurately measuring the sounds of the joints in the clinic and at home; (2) investigate algorithms for quantifying differences between the characteristics of joint sounds of patients with JIA and healthy controls; and (3) conduct a feasibility study in patients with JIA by sending the sensing brace home and evaluating the changes in joint sound characteristics measured throughout treatment. The PI's preliminary data suggests that joint sounds can potentially serve as a physiological biomarker for patients with arthritis. The project will first develop a wearable heterogenous sensor array for acoustical sensing with sufficient battery life. Experiments will collect joint sound data from JIA-affected and healthy patients. Graph mining algorithms will be applied to analyze the high-dimensional joint acoustical emissions data and correlate the data with joint health. At project completion, Dr. Inan will have developed a new physiological measurement tool for diagnosing and treating JIA patients based on their specific symptoms and responses to treatment, ultimately improving their quality of life and minimizing permanent joint damage.
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.
