Classification of sEMG Signals for the Detection of Vocal Fatigue based on VFI Scores
Related Experiment Video
Updated: Feb 2, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Abstract:
In this new research, we expand on our previous system for vocal fatigue detection by adding five new features in the classifier. We also perform further testing on 37 test subjects. The goals were: 1) to classify subjects performing normal versus simulated pressed vocal gestures; 2) to distinguish vocally healthy from vocally fatigued subjects as determined by VFI score on factor 1; and 3) to determine the validity of the labels vis-a-vis the choice of this same VFI-factor-1 boundary. As the results demonstrated, the choice of classifier and the new features were quite appropriate, while there is margin for better choices of the VFI-factor-1 boundary.
More Related Videos
06:22Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
Published on: September 19, 2025
06:16Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
Related Concept Videos
Classification of Signals
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Fatigue
Introduction to z Scores
z scores...
Introduction to z Scores
z scores...
z Scores and Area Under the Curve
Classification of Skeletal Muscle Fibers
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...