A skin-conformable wireless sensor to objectively quantify symptoms of pruritus

Keum San Chun, Youn J. Kang, Jong Yoon Lee, Morgan Nguyen, Brad Lee, Rachel Lee, Han Heul Jo, Emily Allen, Hope Chen, Jungwoo Kim, Lian Yu, Xiaoyue Ni, Kun Hyuck Lee, Hyoyoung Jeong, Joo Hee Lee, Yoonseok Park, Ha Uk Chung, Alvin W. Li, Peter A. Lio, Albert F. YangAnna B. Fishbein, Amy S. Paller, John A. Rogers*, Shuai Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Itch is a common clinical symptom and major driver of disease-related morbidity across a wide range of medical conditions. A substantial unmet need is for objective, accurate measurements of itch. In this article, we present a noninvasive technology to objectively quantify scratching behavior via a soft, flexible, and wireless sensor that captures the acousto-mechanic signatures of scratching from the dorsum of the hand. A machine learning algorithm validated on data collected from healthy subjects (n = 10) indicates excellent performance relative to smartwatch-based approaches. Clinical validation in a cohort of predominately pediatric patients (n = 11) with moderate to severe atopic dermatitis included 46 sleep-nights totaling 378.4 hours. The data indicate an accuracy of 99.0% (84.3% sensitivity, 99.3% specificity) against visual observation. This work suggests broad capabilities relevant to applications ranging from assessing the efficacy of drugs for conditions that cause itch to monitoring disease severity and treatment response.

Original languageEnglish (US)
Article numbereabf9405
JournalScience Advances
Volume7
Issue number18
DOIs
StatePublished - Apr 2021
Externally publishedYes

ASJC Scopus subject areas

  • General

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