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An objective measurement of chronic itch is necessary for improvements inpatient care for numerous medical conditions. While wearables have shownpromise for scratch detection, they are currently unable to estimate scratchintensity, preventing a comprehensive understanding of the effect of itch on anindividual. In this work, we present a framework for the estimation of scratchintensity in addition to the detection of scratch. This is accomplished with amultimodal ring device, consisting of an accelerometer and a contactmicrophone, a pressure-sensitive tablet for capturing ground truth intensityvalues, and machine learning algorithms for regression of scratch intensity ona 0-600 milliwatts (mW) power scale that can be mapped to a 0-10 continuousscale. We evaluate the performance of our algorithms on 20 individuals usingleave one subject out cross-validation and using data from 14 additionalparticipants, we show that our algorithms achieve clinically-relevantdiscrimination of scratching intensity levels. By doing so, our device enablesthe quantification of the substantial variations in the interpretation of the0-10 scale frequently utilized in patient self-reported clinical assessments.This work demonstrates that a finger-worn device can provide multidimensional,objective, real-time measures for the action of scratching.

A Multimodal Sensing Ring for Quantification of Scratch Intensity