Closed-loop network of skin-interfaced wireless devices for quantifying vocal fatigue and providing user feedback

Hyoyoung Jeong, Jae Young Yoo, Wei Ouyang, Aurora Lee Jean Xue Greane, Alexandra Jane Wiebe, Ivy Huang, Young Joong Lee, Jong Yoon Lee, Joohee Kim, Xinchen Ni, Suyeon Kim, Huong Le Thien Huynh, Isabel Zhong, Yu Xuan Chin, Jianyu Gu, Aaron M. Johnson, Theresa Brancaccio, John A. Rogers*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Vocal fatigue is a measurable form of performance fatigue resulting from overuse of the voice and is characterized by negative vocal adaptation. Vocal dose refers to cumulative exposure of the vocal fold tissue to vibration. Professionals with high vocal demands, such as singers and teachers, are especially prone to vocal fatigue. Failure to adjust habits can lead to compensatory lapses in vocal technique and an increased risk of vocal fold injury. Quantifying and recording vocal dose to inform individuals about potential overuse is an important step toward mitigating vocal fatigue. Previous work establishes vocal dosimetry methods, that is, processes to quantify vocal fold vibration dose but with bulky, wired devices that are not amenable to continuous use during natural daily activities; these previously reported systems also provide limited mechanisms for real-time user feedback. This study introduces a soft, wireless, skin-conformal technology that gently mounts on the upper chest to capture vibratory responses associated with vocalization in a manner that is immune to ambient noises. Pairing with a separate, wirelessly linked device supports haptic feedback to the user based on quantitative thresholds in vocal usage. A machine learning-based approach enables precise vocal dosimetry from the recorded data, to support personalized, real-time quantitation and feedback. These systems have strong potential to guide healthy behaviors in vocal use.

Original languageEnglish (US)
Article numbere2219394120
JournalProceedings of the National Academy of Sciences of the United States of America
Volume120
Issue number9
DOIs
StatePublished - Feb 28 2023

Keywords

  • closed-loop network
  • haptic feedback
  • quantifying vocal fatigue
  • real-time machine learning
  • wearable electronics

ASJC Scopus subject areas

  • General

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