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Abstract

Currently, diabetic foot ulcers are difficult to detect accurately and timely, leading to a lot of pain and expense. Current best practice is daily follow-up by people with diabetes along with scheduled follow-up by the incumbent care provider. Although certain indices have been shown to be useful in detecting or predicting ulcers, there is currently no single indicator that can be relied upon for diagnosis. We have developed a prototype multivariable scalable sensor platform that we demonstrate the ability to collect signals about acceleration, rotation, skin electrical response, ambient temperature, humidity , force, real-time skin temperature and bioimpedance data, for later analysis, using the low-cost Raspberry Pi. And Arduino devices. We demonstrate the usefulness of the Raspberry Pi computer in this study of particular interest in electronics - the Raspberry Pi version. We conclude that the presented material shows potential as an adaptive research tool capable of synchronous data collection across multiple sensing modalities. This research tool will be used to optimize sensor selection, placement and algorithm development before translating to a later sock, insole or platform diagnostic device. The combination of several clinically relevant parameters will provide a better understanding of the tissue condition of the foot, but further testing and analysis in volunteers beyond the scope of this article will be reported in the desired time.