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.

翻译：慢性瘙痒的客观测量对于改善多种疾病患者的护理至关重要。尽管可穿戴设备在抓挠检测方面展现出潜力，但目前仍无法评估抓挠强度，从而阻碍了对瘙痒对个体影响的全面理解。本研究提出了一种框架，在检测抓挠行为的同时，还能估计抓挠强度。我们通过一种多模态指环设备实现此目标，该设备包含加速度计、接触式麦克风、用于采集真实强度值的压力敏感平板，以及机器学习算法，可将抓挠强度回归到0-600毫瓦（mW）功率尺度上，并可映射至0-10连续尺度。我们采用留一被试交叉验证法，在20名个体上评估算法性能，并利用另外14名参与者的数据证明，算法能够实现临床相关的抓挠强度等级区分。通过这种方式，该设备能够量化患者自评临床评估中常用0-10量表解读的巨大差异。本研究表明，一种手指佩戴式设备可为抓挠动作提供多维、客观、实时的测量指标。