Learning Pressure Patterns for Patients with Diabetic Foot Syndrome

The diabetic foot syndrome (DFS) is linked to loss of neuron functions, implying that the patients do not feel their feet and may unknowingly injure themselves or apply excessive plantar pressure. Such patients are at 17-40 times higher risk of foot amputation than non-diabetics. Sensor-equipped insoles are being developed to warn diabetics against inadverted excessive pressure. For the successful use of such technology, it is essential to understand how patients distribute plantar pressure load and to identify common pressure patterns, to be later used as basis for recognizing abnormalities. In this study, we propose a mining workflow for the discovery of pressure patterns among DFS patients. Our approach encompasses different ways of modeling pressure distribution among foot regions, and workpaths for the computation of similarity between patients and the construction of clusters of patients who apply pressure on their feet the same way. We report on our findings from a dataset of experiment participants who wore sensor-equipped insoles and were asked to apply and release pressure repeatedly over a time period of several minutes. We elaborate on the pressure patterns thus identified and juxtapose them to findings from the literature.