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

• Published in: Proceedings of the National Academy of Sciences - PNAS 2023-02, Vol.120 (9), p.e2219394120-e2219394120
• Main Authors: Jeong, Hyoyoung, Yoo, Jae-Young, Ouyang, Wei, Greane, Aurora Lee Jean Xue, Wiebe, Alexandra Jane, Huang, Ivy, Lee, Young Joong, Lee, Jong Yoon, Kim, Joohee, Ni, Xinchen, Kim, Suyeon, Huynh, Huong Le-Thien, Zhong, Isabel, Chin, Yu Xuan, Gu, Jianyu, Johnson, Aaron M, Brancaccio, Theresa, Rogers, John A
• Format: Article
• Language: English
• Subjects: Closed loops; Dosimeters; Dosimetry; Fatigue; Fatigue failure; Feedback; Health risks; Humans; Machine learning; Physical Sciences; Quantitation; Real time; Singing; Skin; User feedback; Vibration; Vocal Cords - physiology; Vocal organs; Vocalization behavior; Voice - physiology; Voice Disorders - etiology
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2219394120

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.