# Voice as a sensitive biomarker for predicting exercise intensity: a modelling study

## TL;DR

This study explores how voice characteristics can predict exercise intensity, offering a non-invasive way to monitor physical activity in real time.

## Contribution

The novel contribution is demonstrating that voice-based acoustic features can accurately classify exercise intensity levels.

## Key findings

- Significant variations in speech features like duration, fundamental frequency, and pause times were observed across exercise intensities.
- Statistical models achieved high accuracy in distinguishing different exercise states using voice data.
- Voice analysis shows potential as a real-time, non-invasive tool for monitoring and prescribing exercise intensity.

## Abstract

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 machine (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.