Transformation of Temperature Timeseries into Features that Characterize Patients with Diabetic Autonomic Nerve Disorder

Diabetic foot syndrome is a frequent and serious complication occurring among patients with diabetes. In this study, we investigate the potential of intelligent wearables that monitor temperature changes of the foot surface through temperature sensors. In particular, we are interested in identifying differences between the temperature variations recorded on patients with the disorder and healthy people during an experiment. To this purpose, we propose a method that encompasses shapelet-based timeseries classification and shapelet ranking on predictiveness. We report on our results for an experiment consisting of stance and rest periods of increasing duration.