UCSD’s Joe Wang has developed a totally noninvasive sensor and algorithm to detect glucose levels from sweat on the fingertip. The painless, rapid, and accurate system could revolutionize diabetes management. The systemcombines a simple touch-based fingertip sweat electrochemical sensor with a new algorithm that addresses for personal variations toward the accurate estimate of blood glucose concentrations. It leverages the fast sweat rate on the fingertip for rapid assays of natural perspiration, without any sweat stimulation, along with the personalized sweat-response-to-blood concentration translation. A reliable estimate of the blood glucose sensing concentrations can thus be realized through a simple one-time personal precalibration. Such system training leads to a substantially improved accuracy with a Pearson correlation coefficient higher than 0.95, along with an overall mean absolute relative difference of 7.79%, with 100% paired points residing in the A + B region of the Clarke error grid. The speed and simplicity of the touch-based blood-free fingertip sweat assay, and the elimination of periodic blood calibrations, should lead to frequent self-testing of glucose and enhanced patient compliance toward the improved management of diabetes.
Click to view Joe Wang discussing wearable sensors at they 2019 ApplySci conference at Harvard Medical School.
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