Mij
Senior Member (Voting Rights)
Objective: This study investigates the potential of using voice as a sensitive omics marker to predict exercise intensity.
Methods: 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.
Results: 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.
Conclusion: 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.
LINK
Methods: 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.
Results: 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.
Conclusion: 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.
LINK
