A Multimodal Sensing Ring for Quantification of Scratch Intensity

TL;DR

This paper introduces a multimodal ring device that objectively detects and quantifies scratch intensity, providing a more comprehensive assessment of itch severity for improved patient care.

Contribution

It presents a novel finger-worn device with machine learning algorithms capable of estimating scratch intensity, advancing beyond simple scratch detection.

Findings

Achieves clinically-relevant discrimination of scratch intensity levels

Demonstrates accurate regression of scratch power on a continuous scale

Validates performance on data from 34 individuals

Abstract

An objective measurement of chronic itch is necessary for improvements in patient care for numerous medical conditions. While wearables have shown promise for scratch detection, they are currently unable to estimate scratch intensity, preventing a comprehensive understanding of the effect of itch on an individual. In this work, we present a framework for the estimation of scratch intensity in addition to the detection of scratch. This is accomplished with a multimodal ring device, consisting of an accelerometer and a contact microphone, a pressure-sensitive tablet for capturing ground truth intensity values, and machine learning algorithms for regression of scratch intensity on a 0-600 milliwatts (mW) power scale that can be mapped to a 0-10 continuous scale. We evaluate the performance of our algorithms on 20 individuals using leave one subject out cross-validation and using data from 14 additional participants, we show that our algorithms achieve clinically-relevant discrimination of scratching intensity levels. By doing so, our device enables the quantification of the substantial variations in the interpretation of the 0-10 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.