Padmanabha Akhil, Choudhary Sonal, Majidi Carmel, Erickson Zackory
Robotics Institute, Carnegie Mellon University, Forbes Avenue, Pittsburgh, 15213, PA, USA.
Department of Dermatology, University of Pittsburgh Medical Center, Fifth Avenue, Pittsburgh, 15213, PA, USA.
Commun Med (Lond). 2023 Sep 19;3(1):115. doi: 10.1038/s43856-023-00345-2.
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.
对于改善众多医疗状况下的患者护理而言，慢性瘙痒的客观测量是必要的。虽然可穿戴设备在抓挠检测方面已显示出前景，但它们目前无法估计抓挠强度，从而妨碍了对瘙痒对个体影响的全面理解。
在这项工作中，我们提出了一个除抓挠检测外还能估计抓挠强度的框架。这是通过一个多模态环形设备实现的，该设备由一个加速度计、一个接触式麦克风、一个用于获取真实强度值的压敏平板以及用于在0 - 600毫瓦（mW）功率范围内对抓挠强度进行回归的机器学习算法组成，该功率范围可映射到0 - 10的连续量表。
我们使用留一法交叉验证对20名个体评估了算法的性能，并利用另外14名参与者的数据表明，我们的算法实现了抓挠强度水平的临床相关区分。通过这样做，我们的设备能够量化患者自我报告临床评估中经常使用的0 - 10量表解释中的显著差异。
这项工作表明，一种手指佩戴式设备可以为抓挠行为提供多维、客观、实时的测量。
