Mobile voice health monitoring using a wearable accelerometer sensor and a smartphone platform

Abstract

Many common voice disorders are chronic or recurring conditions that are likely to result from faulty and/or abusive patterns of vocal behavior, referred to generically as vocal hyperfunction. An ongoing goal in clinical voice assessment is the development and use of noninvasively derived measures to quantify and track the daily status of vocal hyperfunction so that the diagnosis and treatment of such behaviorally based voice disorders can be improved. This paper reports on the development of a new, versatile, and cost-effective clinical tool for mobile voice monitoring that acquires the high-bandwidth signal from an accelerometer sensor placed on the neck skin above the collarbone. Using a smartphone as the data acquisition platform, the prototype device provides a user-friendly interface for voice use monitoring, daily sensor calibration, and periodic alert capabilities. Pilot data are reported from three vocally normal speakers and three subjects with voice disorders to demonstrate the potential of the device to yield standard measures of fundamental frequency and sound pressure level and model-based glottal airflow properties. The smartphone-based platform enables future clinical studies for the identification of the best set of measures for differentiating between normal and hyperfunctional patterns of voice use.

Fig. 1

(Color online) Mobile voice monitoring system: (A) Smartphone and accelerometer input, (B) subject wearing the system with wiring underneath clothing and smartphone in belt holster.

Fig. 2

Short-time power spectra of signal (black) and noise (gray) of the accelerometer data from female subject P1.

Fig. 3

(Color online) Acoustic calibration procedure for a sustained vowel produced with increasing loudness. (A) The speaker holds the microphone at a set distance from the lips to derive a (B) linear regression (black line) between the accelerometer signal level (in dBFS; dB relative to full scale) and the acoustic level (in dB SPL). Gray circles indicate dB-dB levels from each 50-ms frame (rectangular window, no overlap).

Fig. 4

(Color online) Model of the subglottal system for impedance-based inverse filtering: (A) Anatomical diagram indicating the location of the accelerometer sensor on the skin surface approximately 5 cm below the glottis (figure adapted from [17]), (B) Mechanoacoustic one-port network indicating frequency-dependent velocities U and impedances Z of the subglottal system sub and subsections sub1 and sub2. Z_skin is the impedance due to the accelerometer sensor load and neck surface properties.

Fig. 5

Voice use profile of subject N2’s first day (24-hour time format). (A) Five-minute moving average of phonation time (gray), average sound pressure level (SPL; lower line), and maximum SPL (upper line) for voiced frames; (B) histogram of fundamental frequency (f0); (C) histogram of SPL; and (D) phonation density showing the relative occurrence of particular combinations of SPL (horizontal axis) and f0 (vertical axis).