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ORIGINAL RESEARCH article
Front. Physiol.
Sec. Exercise Physiology
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1483828
Voice as a Sensitive Omics Marker for Predicting Exercise Intensity: A Modelling Study
Provisionally accepted
Shuyi Zhou 1
Ruisi Ma 1,2 Wangjing Hu 3
Dandan Zhang 4 Rui Hu 5 Shengwei Zou 5 Dingyi Cai 1 Zikang Jiang 1 Hexiao Ding 3
Ting Liu 4*- 1 Jinan University, Guangzhou, Guangdong Province, China
- 2 The Chinese University of Hong Kong, Shatin, China
- 3 Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, China
- 4 School of Nursing, Sun Yat-sen University, Guangzhou, China
- 5 Jiangxi Medical College, Nanchang University, Fuzhou, Fujian Province, China
This study investigates the potential of using voice as a sensitive omics marker to predict exercise intensity.Ninety-two healthy university students aged 18-25 participated in this cross-sectional study, engaging in physical activities of varying intensities, including the Canadian Agility and Movement Skill Assessment (CAMSA), the Plank test, and the Progressive Aerobic Cardiovascular Endurance Run (PACER). Speech data were collected before, during, and after these activities using professional recording equipment. Acoustic features were extracted using the openSMILE toolkit, focusing on the Geneva Minimalistic Acoustic Parameter Set (GeMAPS) and the Computational Paralinguistics Challenge (ComParE) feature sets. These features were analyzed using statistical models, including support vector machines (SVM), to classify exercise intensity.Significant variations in speech characteristics, such as speech duration, fundamental frequency (F0), and pause times, were observed across different exercise intensities, with the models achieving high accuracy in distinguishing between exercise states.These findings suggest that speech analysis can provide a non-invasive, real-time method for monitoring exercise intensity. The study's implications extend to personalized exercise prescriptions, chronic disease management, and the integration of speech analysis into routine health assessments. This approach promotes better exercise adherence and overall health outcomes, highlighting the potential for innovative health monitoring techniques.
Keywords: Speech analysis, Voice, exercise intensity, non-invasive monitoring, Health assessment
Received: 29 Oct 2024; Accepted: 31 Mar 2025.
Copyright: © 2025 Zhou, Ma, Hu, Zhang, Hu, Zou, Cai, Jiang, Ding and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Ting Liu, School of Nursing, Sun Yat-sen University, Guangzhou, 510080, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.