Time Series Features from Foot Temperature Data to Discriminate Between Diabetes-Affected and Healthy Feet

In this work, we describe the use of time series features extracted from foot temperature data obtained from a wearable in-shoe system to discriminate between feet from individuals affected by diabetes, and feet from healthy individuals. We identify a set of features that are statistically significant in discriminating between the two classes and that thus can serve as input to machine learning classifiers.