Cognitive Load Monitoring via Earable Acoustic Sensing
Jiatao Quan, Khaldoon Al-Naimi, Xijia Wei, Yang Liu, Fahim Kawsar, Alessandro Montanari, Ting Dang
Abstract
The rapid adoption of ear-worn devices (earables) has shown significant potential for continuous health monitoring. Despite their close proximity to the human brain and diverse sensing capabilities, the exploration of earable sensing in relation to cognitive function remains underexplored. Building on theoretical and empirical foundations regarding the interplay between cognitive load, auditory complexity, and changes in hearing characteristics influenced by brain function, this study is the first to leverage earable acoustic sensing to assess cognitive load. We specifically designed auditory tasks to elicit four levels of cognitive load and used otoacoustic emissions (OAEs) to measure cochlear response changes in response to cognitive load. By utilizing both audio content indicating auditory complexity and OAEs reflecting hearing characteristic changes, we designed machine learning pipelines to automate the assessment in a four-class cognitive detection task, achieving an accuracy of 68.88%. This research opens a new pathway for using earable acoustic sensing in monitoring cognitive function and holds great potential for future cognitive augmentation.
BibTeX
@inproceedings{icassp2025_cognitiveloadmon,
title = {Cognitive Load Monitoring via Earable Acoustic Sensing},
author = {Jiatao Quan and Khaldoon Al-Naimi and Xijia Wei and Yang Liu and Fahim Kawsar and Alessandro Montanari and Ting Dang},
booktitle = {ICASSP 2025},
year = {2025}
}