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Updated: Jul 6, 2026
Hemi-laryngeal Setup for Studying Vocal Fold Vibration in Three Dimensions
Published on: November 25, 2017
Physiological Validation of Glottal Airflow Estimation Using Neck-Surface Accelerometry: A Comparison With
Youjin Kang1, Dahyung Han2, Boram Yun3
1Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea.
Objectives:
Neck-surface accelerometers (ACC) are promising for ambulatory monitoring, yet their potential requires evaluation beyond simple aerodynamics. As vocal fold collision energy is transmitted through neck tissue, the ACC signal is hypothesized to encompass both aerodynamic and collision-related characteristics. This study investigated how effectively the ACC reflects glottal collision dynamics by comparing ACC-derived metrics with electroglottography (EGG) across diverse phonations.
Methods:
Synchronized ACC, MIC, and EGG signals were collected from nine vocally healthy adults during sustained phonation in four distinct types: modal, breathy, falsetto, and creaky. Glottal airflow parameters were estimated from ACC and MIC signals using inverse filtering techniques to analyze aerodynamic changes across phonation types and to assess inter-subject variability. The estimated parameters were evaluated in relation to EGG-derived contact measures, including contact quotient and open quotient, using correlation and regression analyses.
Results:
While ACC and MIC signals shared similar trends in AQ and MFDR, distinct behaviors emerged in NAQ and H1-H2, reflecting physical differences in their respective transmission paths. ACC-based NAQ (r = -0.830) and H1-H2 (r = -0.848) demonstrated significantly stronger correlations with the EGG contact quotient than MIC-based estimates. Linear regression revealed higher explanatory power (R2) for ACC-derived metrics, confirming that the sensor encompasses more direct information regarding glottal collision dynamics than conventional acoustic signals.
Conclusions:
These results support the central hypothesis that the ACC faithfully reflects the mechanical energy of vocal fold collision, offering a more direct representation of contact dynamics than conventional acoustic signals. This study establishes the ACC as a multifaceted foundational tool for biomechanical-based wearable voice assessment systems, extending its utility to the monitoring of mechanical load in daily voice care.
