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Pupillometry to Assess Auditory Sensation in Guinea Pigs

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Multimodal Cognitive Load Estimation With Radio Frequency Sensing and Pupillometry in Complex Auditory Environments.

Usman Anwar, Adeel Hussain, Mandar Gogate

IEEE Journal of Biomedical and Health Informatics

|November 19, 2025

Summary

This summary is machine-generated.

This study introduces a novel, non-invasive method using Radio Frequency (RF) and pupillometry to detect listening effort and cognitive load (CL). The approach offers a privacy-preserving alternative to traditional sensors, improving CL estimation accuracy.

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Detecting listening effort and cognitive load (CL) is challenging with current privacy-invasive and complex methods.
Existing techniques face issues with synchronization, data alignment, and accessibility, leading to inaccurate CL estimates.

Purpose of the Study:

To develop a multi-modal, non-invasive, and privacy-preserving approach for estimating cognitive load (CL) and listening effort.
To combine Radio Frequency (RF) sensing with pupillometry for a more robust CL assessment.

Main Methods:

Designed custom RF sensors to measure blood flow changes in brain regions with high spatial resolution.
Integrated RF sensing with pupillometry (measuring pupil size and dilation) for multi-modal fusion.
Collected a novel multi-modal dataset in a controlled environment with varying noise levels.

Main Results:

Pupillometry data showed high reliability (average ICC > 0.95).
Strong correlation established between pupillometry and RF data (average PCC > 0.79).
K-means clustering successfully classified CL into high and low categories using RF data.

Conclusions:

The combined RF and pupillometry approach provides a robust and accurate method for estimating listening effort and cognitive load.
This non-invasive technique overcomes limitations of conventional methods.
Future applications include integrating RF sensors into glasses for hearing aid users and optimizing speech enhancement.