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Voice as a sensitive biomarker for predicting exercise intensity: a modelling study
Shuyi Zhou1, Ruisi Ma1, Wangjing Hu2
1School of Physical Education, Jinan University, Guangzhou, Guangdong, China.
Frontiers in Physiology
|May 13, 2025
Summary
Voice analysis accurately predicts exercise intensity, offering a non-invasive method for real-time monitoring. This omics marker has potential for personalized fitness and health management.
Area of Science:
- Exercise Physiology
- Speech Science
- Biomedical Engineering
Background:
- Monitoring exercise intensity is crucial for effective training and health management.
- Traditional methods can be invasive or lack real-time feedback capabilities.
- Exploring novel biomarkers like voice offers a promising alternative.
Purpose of the Study:
- To investigate voice as a sensitive omics marker for predicting exercise intensity.
- To evaluate the accuracy of speech analysis in differentiating exercise states.
- To explore the potential of voice analysis in health and fitness applications.
Main Methods:
- Ninety-two university students performed varied intensity exercises (CAMSA, Plank, PACER).
- Speech data collected pre-, during, and post-exercise using professional equipment.
- Acoustic features (GeMAPS, ComParE) analyzed with machine learning (SVM) for intensity classification.
Main Results:
- Significant speech variations (duration, F0, pauses) correlated with exercise intensity.
- Machine learning models achieved high accuracy in distinguishing exercise states.
- Voice analysis demonstrated effectiveness in real-time exercise intensity prediction.
Conclusions:
- Speech analysis offers a non-invasive, real-time method for monitoring exercise intensity.
- Potential applications include personalized exercise prescriptions and chronic disease management.
- Integrates innovative health monitoring for improved adherence and outcomes.
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