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.

Strain wave gears are frequently used in small and medium size industrial robots. In order to describe and quantify friction effects in gearboxes of such type, a structurally simple, yet powerful model is proposed taking into account both speed-and load-dependent friction effects. Moreover, position-dependent disturbances in a robotic joint are considered. An identification procedure is presented that allows to separate the individual components of the model and identify them subsequently. The effectiveness of the model and identification procedure is validated using experimental data gathered from four different robotic joints of varying size. Furthermore, the benefits of improved friction modeling are shown by means of different applications, including smooth lead-through programming and sensorless force control.

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.